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ct(Ve.Layout,null,{})},enhanceApp({app:n,router:e,siteData:t}){jr(n)}};export{Gr as R,Xs as c,V as u}; diff --git a/previews/PR53/assets/constraints_comparison_constraints.md._xAR0god.js b/previews/PR53/assets/constraints_comparison_constraints.md.BzzoUfWs.js similarity index 98% rename from previews/PR53/assets/constraints_comparison_constraints.md._xAR0god.js rename to previews/PR53/assets/constraints_comparison_constraints.md.BzzoUfWs.js index 820bb58..6dad81f 100644 --- a/previews/PR53/assets/constraints_comparison_constraints.md._xAR0god.js +++ b/previews/PR53/assets/constraints_comparison_constraints.md.BzzoUfWs.js @@ -1,4 +1,4 @@ -import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.B__MqT43.js";const o=JSON.parse('{"title":"Constraints.jl: Streamlining Constraint Definition and Integration in Julia","description":"","frontmatter":{},"headers":[],"relativePath":"constraints/comparison_constraints.md","filePath":"constraints/comparison_constraints.md","lastUpdated":null}'),h={name:"constraints/comparison_constraints.md"},t=n(`

Constraints.jl: Streamlining Constraint Definition and Integration in Julia ​

julia
using Constraints
+import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.B__MqT43.js";const F=JSON.parse('{"title":"Constraints.jl: Streamlining Constraint Definition and Integration in Julia","description":"","frontmatter":{},"headers":[],"relativePath":"constraints/comparison_constraints.md","filePath":"constraints/comparison_constraints.md","lastUpdated":null}'),h={name:"constraints/comparison_constraints.md"},t=n(`
Constraints.jl: Streamlining Constraint Definition and Integration in Julia ​
julia
using Constraints
 
 concept(:all_different, [1,1,1,2]) # false
 concept(:all_different, [1,9,3,2]) # true
julia
using Constraints
@@ -17,7 +17,7 @@ import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.B__MqT43.js";const o
 JuMP.optimize!(model)
 @info "All Different" value.(X) value.(Y)
 
-# Note that this example gives a solution for the all_different constraint.
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
+# Note that this example gives a solution for the all_different constraint.
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
 
 concept(:all_equal, [1,1,1,2]) #false
 concept(:all_equal, [1,1,1,1]) #true
julia
using Constraints
@@ -35,7 +35,7 @@ import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.B__MqT43.js";const o
 JuMP.optimize!(model)
 @info "All Equal" value.(X)
 
-# Note that this example gives a solution for the all_equal constraint.
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
+# Note that this example gives a solution for the all_equal constraint.
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
 
 @info concept(:ordered, [1, 2, 3, 4, 4]; op=≤)
 @info concept(:ordered, [1, 2, 3, 3, 5]; op=<)
@@ -79,4 +79,4 @@ import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.B__MqT43.js";const o
 c([1, 2, 3, 4, 4]; op=≤)
 c([1, 2, 3, 4, 5]; op=<)
 !c([1, 2, 3, 4, 3]; op=≤)
-!c([1, 2, 3, 4, 3]; op=<)
source

`,11),l=[t];function k(p,e,E,r,d,g){return a(),i("div",null,l)}const F=s(h,[["render",k]]);export{o as __pageData,F as default};
+!c([1, 2, 3, 4, 3]; op=<)

source


`,11),l=[t];function k(p,e,E,r,d,g){return a(),i("div",null,l)}const o=s(h,[["render",k]]);export{F as __pageData,o as default}; diff --git a/previews/PR53/assets/constraints_comparison_constraints.md._xAR0god.lean.js b/previews/PR53/assets/constraints_comparison_constraints.md.BzzoUfWs.lean.js similarity index 71% rename from previews/PR53/assets/constraints_comparison_constraints.md._xAR0god.lean.js rename to previews/PR53/assets/constraints_comparison_constraints.md.BzzoUfWs.lean.js index 60f7785..7926a12 100644 --- a/previews/PR53/assets/constraints_comparison_constraints.md._xAR0god.lean.js +++ b/previews/PR53/assets/constraints_comparison_constraints.md.BzzoUfWs.lean.js @@ -1 +1 @@ -import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.B__MqT43.js";const o=JSON.parse('{"title":"Constraints.jl: Streamlining Constraint Definition and Integration in Julia","description":"","frontmatter":{},"headers":[],"relativePath":"constraints/comparison_constraints.md","filePath":"constraints/comparison_constraints.md","lastUpdated":null}'),h={name:"constraints/comparison_constraints.md"},t=n("",11),l=[t];function k(p,e,E,r,d,g){return a(),i("div",null,l)}const F=s(h,[["render",k]]);export{o as __pageData,F as default}; +import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.B__MqT43.js";const F=JSON.parse('{"title":"Constraints.jl: Streamlining Constraint Definition and Integration in Julia","description":"","frontmatter":{},"headers":[],"relativePath":"constraints/comparison_constraints.md","filePath":"constraints/comparison_constraints.md","lastUpdated":null}'),h={name:"constraints/comparison_constraints.md"},t=n("",11),l=[t];function k(p,e,E,r,d,g){return a(),i("div",null,l)}const o=s(h,[["render",k]]);export{F as __pageData,o as default}; diff --git a/previews/PR53/assets/constraints_connection_constraints.md.DEFHJFPq.js b/previews/PR53/assets/constraints_connection_constraints.md.B5jEZYFf.js similarity index 98% rename from previews/PR53/assets/constraints_connection_constraints.md.DEFHJFPq.js rename to previews/PR53/assets/constraints_connection_constraints.md.B5jEZYFf.js index f87d329..7363ac7 100644 --- a/previews/PR53/assets/constraints_connection_constraints.md.DEFHJFPq.js +++ b/previews/PR53/assets/constraints_connection_constraints.md.B5jEZYFf.js @@ -1,4 +1,4 @@ -import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.B__MqT43.js";const F=JSON.parse('{"title":"Constraints.jl: Streamlining Constraint Definition and Integration in Julia","description":"","frontmatter":{},"headers":[],"relativePath":"constraints/connection_constraints.md","filePath":"constraints/connection_constraints.md","lastUpdated":null}'),h={name:"constraints/connection_constraints.md"},k=n(`

Constraints.jl: Streamlining Constraint Definition and Integration in Julia ​

julia
using Constraints
+import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.B__MqT43.js";const F=JSON.parse('{"title":"Constraints.jl: Streamlining Constraint Definition and Integration in Julia","description":"","frontmatter":{},"headers":[],"relativePath":"constraints/connection_constraints.md","filePath":"constraints/connection_constraints.md","lastUpdated":null}'),h={name:"constraints/connection_constraints.md"},k=n(`
Constraints.jl: Streamlining Constraint Definition and Integration in Julia ​
julia
using Constraints
 
 concept(:maximum, [1,1,1,2], val = 2, op = ==) # true
 concept(:maximum, [1,2,4,4], val = 2, op = ==) # false
julia
using Constraints
@@ -15,7 +15,7 @@ import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.B__MqT43.js";const F
 @variable(model, 1≤X[1:5]≤5, Int)
 @constraint(model, X in Maximum(; op = ==, val = 5))
 optimize!(model)
-@info "Maximum" value.(X)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
+@info "Maximum" value.(X)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
 
 concept(:minimum, [1,1,1,2], val = 1, op = ==) # true
 concept(:minimum, [1,2,4,4], val = 2, op = ==) # false
julia
using Constraints
@@ -34,7 +34,7 @@ import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.B__MqT43.js";const F
 JuMP.optimize!(model)
 @info "Minimum" value.(X)
 
-# Note that this example gives a solution for the minimum constraint.
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
+# Note that this example gives a solution for the minimum constraint.
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
 
 @info concept(:element, [1, 2, 3, 4, 5]; id=1, val=1)
 @info concept(:element, [1, 2, 3, 4, 5]; id=1, val=2)
@@ -58,7 +58,7 @@ import{_ as s,c as i,o as a,a7 as n}from"./chunks/framework.B__MqT43.js";const F
 @constraint(model, Y in Element(; id = 1, val = 1))
 @constraint(model, Z in Element(; id = 2, val = 2))
 JuMP.optimize!(model)
-@info "Element" value.(X) value.(Y) value.(Z)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
+@info "Element" value.(X) value.(Y) value.(Z)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
 
 @info concept(:channel, [2, 1, 4, 3])
 @info concept(:channel, [1, 2, 3, 4])
diff --git a/previews/PR53/assets/constraints_connection_constraints.md.DEFHJFPq.lean.js b/previews/PR53/assets/constraints_connection_constraints.md.B5jEZYFf.lean.js
similarity index 100%
rename from previews/PR53/assets/constraints_connection_constraints.md.DEFHJFPq.lean.js
rename to previews/PR53/assets/constraints_connection_constraints.md.B5jEZYFf.lean.js
diff --git a/previews/PR53/assets/constraints_counting_summing_constraints.md.BtqJIwAJ.js b/previews/PR53/assets/constraints_counting_summing_constraints.md.BrFsDYjC.js
similarity index 98%
rename from previews/PR53/assets/constraints_counting_summing_constraints.md.BtqJIwAJ.js
rename to previews/PR53/assets/constraints_counting_summing_constraints.md.BrFsDYjC.js
index 4a3c65c..4330b3b 100644
--- a/previews/PR53/assets/constraints_counting_summing_constraints.md.BtqJIwAJ.js
+++ b/previews/PR53/assets/constraints_counting_summing_constraints.md.BrFsDYjC.js
@@ -1,4 +1,4 @@
-import{_ as s,c as i,o as a,a7 as h}from"./chunks/framework.B__MqT43.js";const F=JSON.parse('{"title":"Constraints.jl: Streamlining Constraint Definition and Integration in Julia","description":"","frontmatter":{},"headers":[],"relativePath":"constraints/counting_summing_constraints.md","filePath":"constraints/counting_summing_constraints.md","lastUpdated":null}'),k={name:"constraints/counting_summing_constraints.md"},n=h(`
Constraints.jl: Streamlining Constraint Definition and Integration in Julia ​
julia
using Constraints
+import{_ as s,c as i,o as a,a7 as h}from"./chunks/framework.B__MqT43.js";const F=JSON.parse('{"title":"Constraints.jl: Streamlining Constraint Definition and Integration in Julia","description":"","frontmatter":{},"headers":[],"relativePath":"constraints/counting_summing_constraints.md","filePath":"constraints/counting_summing_constraints.md","lastUpdated":null}'),k={name:"constraints/counting_summing_constraints.md"},n=h(`
Constraints.jl: Streamlining Constraint Definition and Integration in Julia ​
julia
using Constraints
 
 @info concept(:sum, [1, 2, 3, 4, 5]; op = ==, val=15)
 @info concept(:sum, [1, 2, 3, 4, 5]; op = ==, val=2)
@@ -20,7 +20,7 @@ import{_ as s,c as i,o as a,a7 as h}from"./chunks/framework.B__MqT43.js";const F
 @constraint(model, X in Sum(; op = ==, val = 15))
 @constraint(model, Y in Sum(; op = <=, val = 10))
 JuMP.optimize!(model)
-@info "Sum" value.(X) value.(Y)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
+@info "Sum" value.(X) value.(Y)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
 
 concept(:count, [1,1,1,2], vals = [1, 1, 1, 2], op = ==, val = 4) # true
 concept(:count, [1,1,1,2], vals = [1, 1, 1, 2], op = ==, val = 5) # false
@@ -54,7 +54,7 @@ import{_ as s,c as i,o as a,a7 as h}from"./chunks/framework.B__MqT43.js";const F
 @constraint(model, X_at_most in AtMost(vals = [1, 2], val = 1))
 @constraint(model, X_exactly in Exactly(vals = [1, 2], val = 2))
 JuMP.optimize!(model)
-@info "Count" value.(X) value.(X_at_least) value.(X_at_most) value.(X_exactly)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
+@info "Count" value.(X) value.(X_at_least) value.(X_at_most) value.(X_exactly)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
 
 @info concept(:nvalues, [1, 2, 3, 4, 5]; op = ==, val = 5)
 @info concept(:nvalues, [1, 2, 3, 4, 5]; op = ==, val = 2)
@@ -78,7 +78,7 @@ import{_ as s,c as i,o as a,a7 as h}from"./chunks/framework.B__MqT43.js";const F
 @constraint(model, Y in NValues(; op = ==, val = 2))
 @constraint(model, Z in NValues(; op = <=, val = 5, vals = [1, 2]))
 JuMP.optimize!(model)
-@info "NValues" value.(X) value.(Y) value.(Z)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
+@info "NValues" value.(X) value.(Y) value.(Z)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
 
 # [v1, v2, v3], [v1, a1, a2; v2, b1, b2; v3, c1, c2] means v1 occurs between a1 and a2 times in the first array, similar for v2 and v3.
 
@@ -143,4 +143,4 @@ import{_ as s,c as i,o as a,a7 as h}from"./chunks/framework.B__MqT43.js";const F
 cc([8, 5, 10, 10]; vals=[2 0 1; 5 1 3; 10 2 3])
 
 co = concept(:cardinality_open)
-co([8, 5, 10, 10]; vals=[2 0 1; 5 1 3; 10 2 3])
source

`,14),t=[n];function l(p,e,E,d,r,g){return a(),i("div",null,t)}const C=s(k,[["render",l]]);export{F as __pageData,C as default};
+co([8, 5, 10, 10]; vals=[2 0 1; 5 1 3; 10 2 3])
source

`,14),l=[n];function t(p,e,E,d,r,g){return a(),i("div",null,l)}const C=s(k,[["render",t]]);export{F as __pageData,C as default};
diff --git a/previews/PR53/assets/constraints_counting_summing_constraints.md.BtqJIwAJ.lean.js b/previews/PR53/assets/constraints_counting_summing_constraints.md.BrFsDYjC.lean.js
similarity index 69%
rename from previews/PR53/assets/constraints_counting_summing_constraints.md.BtqJIwAJ.lean.js
rename to previews/PR53/assets/constraints_counting_summing_constraints.md.BrFsDYjC.lean.js
index 639d5c4..7d4283e 100644
--- a/previews/PR53/assets/constraints_counting_summing_constraints.md.BtqJIwAJ.lean.js
+++ b/previews/PR53/assets/constraints_counting_summing_constraints.md.BrFsDYjC.lean.js
@@ -1 +1 @@
-import{_ as s,c as i,o as a,a7 as h}from"./chunks/framework.B__MqT43.js";const F=JSON.parse('{"title":"Constraints.jl: Streamlining Constraint Definition and Integration in Julia","description":"","frontmatter":{},"headers":[],"relativePath":"constraints/counting_summing_constraints.md","filePath":"constraints/counting_summing_constraints.md","lastUpdated":null}'),k={name:"constraints/counting_summing_constraints.md"},n=h("",14),t=[n];function l(p,e,E,d,r,g){return a(),i("div",null,t)}const C=s(k,[["render",l]]);export{F as __pageData,C as default};
+import{_ as s,c as i,o as a,a7 as h}from"./chunks/framework.B__MqT43.js";const F=JSON.parse('{"title":"Constraints.jl: Streamlining Constraint Definition and Integration in Julia","description":"","frontmatter":{},"headers":[],"relativePath":"constraints/counting_summing_constraints.md","filePath":"constraints/counting_summing_constraints.md","lastUpdated":null}'),k={name:"constraints/counting_summing_constraints.md"},n=h("",14),l=[n];function t(p,e,E,d,r,g){return a(),i("div",null,l)}const C=s(k,[["render",t]]);export{F as __pageData,C as default};
diff --git a/previews/PR53/assets/constraints_generic_constraints.md.BMouGtoq.js b/previews/PR53/assets/constraints_generic_constraints.md.BMouGtoq.js
new file mode 100644
index 0000000..192c38d
--- /dev/null
+++ b/previews/PR53/assets/constraints_generic_constraints.md.BMouGtoq.js
@@ -0,0 +1,90 @@
+import{_ as e,c as a,j as s,a as i,a7 as n,o as t}from"./chunks/framework.B__MqT43.js";const vs=JSON.parse('{"title":"Generic Constraints","description":"","frontmatter":{},"headers":[],"relativePath":"constraints/generic_constraints.md","filePath":"constraints/generic_constraints.md","lastUpdated":null}'),l={name:"constraints/generic_constraints.md"},h=n('
Generic Constraints ​
In the XCSP³-core standard, generic constraints are categorized into two main types: intention and extension constraints.
Intention Constraints ​
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Note that the intention constraint is not directly available through the JC-API in Constraints.jl. It is designed as such since defining a constraint through a predicate is the natural way.
We provide a straightforward example through the :dist_different constraint on how to define and add such a constraint in the USUAL_CONSTRAINTS collection.
Higher level modeling languages such as JuMP should provide a Intention interface.
Defining an intention constraint in JC-API ​
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The constraint is then added to the usual constraints collection.
julia
const description_dist_different = """
+Ensures that the distances between marks on the ruler are unique.
+"""
+
+# Define the predicate
+predicate_dist_different(x) = abs(x[1] - x[2]) ≠ abs(x[3] - x[4])
+
+# Add it to usual constraints
+@usual concept_dist_different(x) = xcsp_intention(
+    list = x,
+    predicate = predicate_dist_different
+)
Please check the section dedicated to the Golomb Ruler problem to see a use for this constraint. <!– TODO: Golomb Ruler –>
APIs ​
Note that the intention constraint is not directly available through the JC-API in Constraints.jl. It is designed as such since defining a constraint through a predicate is the natural way.
We provide here a usage example for the :dist_different constraint, previously added to the USUAL_CONSTRAINTS collection.
Higher level modeling language such as JuMP should provide an Intention interface.
julia
using Constraints
+
+concept(:dist_different, x)
+concept(:dist_different)(x)
julia
# Defines the DistDifferent constraint
+using Constraints
+
+c = x -> xcsp_intention(
+    list = x,
+    predicate = y -> abs(y[1] - y[2]) ≠ abs(y[3] - y[4])
+)
+
+c([1, 2, 3, 3]) # true
+c([1, 2, 3, 4]) # false
julia
using CBLS, JuMP
+
+model = Model(CBLS.Optimizer)
+
+# Using build-in DistDifferent
+@variable(model, 0 <= X[1:4] <= 10, Int)
+@constraint(model, X in DistDifferent())
+
+# Alternatively
+@variable(model, 0 <= Y[1:4] <= 10, Int)
+@constraint(model, Y in Intention(y -> abs(y[1] - y[2]) ≠ abs(y[3] - y[4])))
+
+optimize!(model)
+
+@info value.(X)
+@info value.(Y)
julia
using CBLS
+import MathOptInterface as MOI
+
+optimizer = CBLS.Optimizer()
+
+x = MOI.add_variables(optimizer, 4)
+for xi in x
+    # Missing RangeDomain currently in CBLS
+    MOI.add_constraint(optimizer, xi, CBLS.DiscreteSet(collect[1:10]))
+end
+MOI.add_constraint(optimizer, x, CBLS.Intention(y -> abs(y[1] - y[2]) ≠ abs(y[3] - y[4])))
+
+MOI.optimize!(optimizer)
Extension Constraints ​
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These two types of constraints provide a flexible way to define complex relationships between variables in constraint programming.
julia
using Constraints
+
+concept(:dist_different, x)
+concept(:dist_different)(x)
julia
# Defines the DistDifferent constraint
+using Constraints
+
+c = x -> xcsp_intention(
+    list = x,
+    predicate = y -> abs(y[1] - y[2]) ≠ abs(y[3] - y[4])
+)
+
+c([1, 2, 3, 3]) # true
+c([1, 2, 3, 4]) # false
julia
using CBLS, JuMP
+
+model = Model(CBLS.Optimizer)
+
+# Using build-in DistDifferent
+@variable(model, 0 <= X[1:4] <= 10, Int)
+@constraint(model, X in DistDifferent())
+
+# Alternatively
+@variable(model, 0 <= Y[1:4] <= 10, Int)
+@constraint(model, Y in Intention(y -> abs(y[1] - y[2]) ≠ abs(y[3] - y[4])))
+
+optimize!(model)
+
+@info value.(X)
+@info value.(Y)
julia
using CBLS
+import MathOptInterface as MOI
+
+optimizer = CBLS.Optimizer()
+
+x = MOI.add_variables(optimizer, 4)
+for xi in x
+    # Missing RangeDomain currently in CBLS
+    MOI.add_constraint(optimizer, xi, CBLS.DiscreteSet(collect[1:10]))
+end
+MOI.add_constraint(optimizer, x, CBLS.Intention(y -> abs(y[1] - y[2]) ≠ abs(y[3] - y[4])))
+
+MOI.optimize!(optimizer)
`,2);function Cs(us,Fs,xs,ws,_s,fs){return t(),a("div",null,[h,s("p",null,[i("These are constraints that are defined by a logical expression or a function. They are called intentional because they are defined by the property they satisfy. For example, a constraint that specifies that a variable "),s("mjx-container",p,[(t(),a("svg",k,d)),o]),i(" must be less than a variable "),s("mjx-container",g,[(t(),a("svg",T,c)),Q]),i(" could be defined intentionally as "),s("mjx-container",y,[(t(),a("svg",m,u)),F]),i(".")]),x,s("p",null,[i("We use the "),w,i(" constraint to illustrate how to define an intention constraint in "),_,i(". The "),f,i(" constraint ensures that the distances between marks "),s("mjx-container",b,[(t(),a("svg",v,D)),B]),i(" on a ruler are unique.")]),s("mjx-container",A,[(t(),a("svg",V,L)),j]),S,s("p",null,[i("These are constraints that are defined by explicitly listing all the tuples of values that satisfy the constraint. They are called extensional because they are defined by the set of values they allow. For example, a binary constraint that specifies that a variable "),s("mjx-container",Z,[(t(),a("svg",I,z)),O]),i(" must be either "),s("mjx-container",J,[(t(),a("svg",R,q)),N]),i(" or "),s("mjx-container",U,[(t(),a("svg",X,W)),$]),i(" and a variable "),s("mjx-container",K,[(t(),a("svg",ss,as)),ts]),i(" must be either "),s("mjx-container",ns,[(t(),a("svg",es,hs)),ps]),i(" or "),s("mjx-container",ks,[(t(),a("svg",rs,os)),gs]),i(" could be defined extensionally by the set of tuples "),s("mjx-container",Ts,[(t(),a("svg",Es,Qs)),ys])]),ms])}const Hs=e(l,[["render",Cs]]);export{vs as __pageData,Hs as default};
diff --git a/previews/PR53/assets/constraints_generic_constraints.md.BMouGtoq.lean.js b/previews/PR53/assets/constraints_generic_constraints.md.BMouGtoq.lean.js
new file mode 100644
index 0000000..18c31eb
--- /dev/null
+++ b/previews/PR53/assets/constraints_generic_constraints.md.BMouGtoq.lean.js
@@ -0,0 +1 @@
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diff --git a/previews/PR53/assets/constraints_generic_constraints.md.CtmFhNuA.js b/previews/PR53/assets/constraints_generic_constraints.md.CtmFhNuA.js
deleted file mode 100644
index d1efe95..0000000
--- a/previews/PR53/assets/constraints_generic_constraints.md.CtmFhNuA.js
+++ /dev/null
@@ -1,50 +0,0 @@
-import{_ as e,c as a,j as s,a as i,a7 as n,o as t}from"./chunks/framework.B__MqT43.js";const O=JSON.parse('{"title":"Generic Constraints","description":"","frontmatter":{},"headers":[],"relativePath":"constraints/generic_constraints.md","filePath":"constraints/generic_constraints.md","lastUpdated":null}'),l={name:"constraints/generic_constraints.md"},h=n('
Generic Constraints ​
In the XCSP³-core standard, generic constraints are categorized into two main types: intention and extension constraints.
Intention Constraints ​
',3),p={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},k={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.025ex"},xmlns:"http://www.w3.org/2000/svg",width:"1.294ex",height:"1.025ex",role:"img",focusable:"false",viewBox:"0 -442 572 453","aria-hidden":"true"},r=s("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[s("g",{"data-mml-node":"math"},[s("g",{"data-mml-node":"mi"},[s("path",{"data-c":"1D465",d:"M52 289Q59 331 106 386T222 442Q257 442 286 424T329 379Q371 442 430 442Q467 442 494 420T522 361Q522 332 508 314T481 292T458 288Q439 288 427 299T415 328Q415 374 465 391Q454 404 425 404Q412 404 406 402Q368 386 350 336Q290 115 290 78Q290 50 306 38T341 26Q378 26 414 59T463 140Q466 150 469 151T485 153H489Q504 153 504 145Q504 144 502 134Q486 77 440 33T333 -11Q263 -11 227 52Q186 -10 133 -10H127Q78 -10 57 16T35 71Q35 103 54 123T99 143Q142 143 142 101Q142 81 130 66T107 46T94 41L91 40Q91 39 97 36T113 29T132 26Q168 26 194 71Q203 87 217 139T245 247T261 313Q266 340 266 352Q266 380 251 392T217 404Q177 404 142 372T93 290Q91 281 88 280T72 278H58Q52 284 52 289Z",style:{"stroke-width":"3"}})])])],-1),d=[r],o=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"x")])],-1),c={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},E={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.464ex"},xmlns:"http://www.w3.org/2000/svg",width:"1.109ex",height:"1.464ex",role:"img",focusable:"false",viewBox:"0 -442 490 647","aria-hidden":"true"},g=s("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[s("g",{"data-mml-node":"math"},[s("g",{"data-mml-node":"mi"},[s("path",{"data-c":"1D466",d:"M21 287Q21 301 36 335T84 406T158 442Q199 442 224 419T250 355Q248 336 247 334Q247 331 231 288T198 191T182 105Q182 62 196 45T238 27Q261 27 281 38T312 61T339 94Q339 95 344 114T358 173T377 247Q415 397 419 404Q432 431 462 431Q475 431 483 424T494 412T496 403Q496 390 447 193T391 -23Q363 -106 294 -155T156 -205Q111 -205 77 -183T43 -117Q43 -95 50 -80T69 -58T89 -48T106 -45Q150 -45 150 -87Q150 -107 138 -122T115 -142T102 -147L99 -148Q101 -153 118 -160T152 -167H160Q177 -167 186 -165Q219 -156 247 -127T290 -65T313 -9T321 21L315 17Q309 13 296 6T270 -6Q250 -11 231 -11Q185 -11 150 11T104 82Q103 89 103 113Q103 170 138 262T173 379Q173 380 173 381Q173 390 173 393T169 400T158 404H154Q131 404 112 385T82 344T65 302T57 280Q55 278 41 278H27Q21 284 21 287Z",style:{"stroke-width":"3"}})])])],-1),y=[g],C=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"y")])],-1),u={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},Q={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.464ex"},xmlns:"http://www.w3.org/2000/svg",width:"5.42ex",height:"1.686ex",role:"img",focusable:"false",viewBox:"0 -540 2395.6 745","aria-hidden":"true"},m=n('',1),T=[m],F=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"x"),s("mo",null,"<"),s("mi",null,"y")])],-1),x=n('
Note that the intention constraint is not directly available through the JC-API in Constraints.jl. It is designed as such since defining a constraint through a predicate is the natural way.
We provide a straightforward example through the :dist_different constraint on how to define and add such a constraint in the USUAL_CONSTRAINTS collection.
Higher level modeling languages such as JuMP should provide a Intention interface.
Defining an intention constraint in JC-API ​
',4),b=s("code",null,"dist_different",-1),f=s("em",null,"Constraints.jl",-1),v=s("code",null,"dist_different",-1),_={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},B={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.025ex"},xmlns:"http://www.w3.org/2000/svg",width:"1.294ex",height:"1.025ex",role:"img",focusable:"false",viewBox:"0 -442 572 453","aria-hidden":"true"},w=s("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[s("g",{"data-mml-node":"math"},[s("g",{"data-mml-node":"mi"},[s("path",{"data-c":"1D465",d:"M52 289Q59 331 106 386T222 442Q257 442 286 424T329 379Q371 442 430 442Q467 442 494 420T522 361Q522 332 508 314T481 292T458 288Q439 288 427 299T415 328Q415 374 465 391Q454 404 425 404Q412 404 406 402Q368 386 350 336Q290 115 290 78Q290 50 306 38T341 26Q378 26 414 59T463 140Q466 150 469 151T485 153H489Q504 153 504 145Q504 144 502 134Q486 77 440 33T333 -11Q263 -11 227 52Q186 -10 133 -10H127Q78 -10 57 16T35 71Q35 103 54 123T99 143Q142 143 142 101Q142 81 130 66T107 46T94 41L91 40Q91 39 97 36T113 29T132 26Q168 26 194 71Q203 87 217 139T245 247T261 313Q266 340 266 352Q266 380 251 392T217 404Q177 404 142 372T93 290Q91 281 88 280T72 278H58Q52 284 52 289Z",style:{"stroke-width":"3"}})])])],-1),H=[w],A=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"x")])],-1),D={class:"MathJax",jax:"SVG",display:"true",style:{direction:"ltr",display:"block","text-align":"center",margin:"1em 0",position:"relative"}},V={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.566ex"},xmlns:"http://www.w3.org/2000/svg",width:"25.797ex",height:"2.262ex",role:"img",focusable:"false",viewBox:"0 -750 11402.4 1000","aria-hidden":"true"},M=n('',1),j=[M],L=s("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[s("mo",{"data-mjx-texclass":"ORD",stretchy:"false"},"|"),s("mi",null,"x"),s("mo",{stretchy:"false"},"["),s("mn",null,"1"),s("mo",{stretchy:"false"},"]"),s("mo",null,"−"),s("mi",null,"x"),s("mo",{stretchy:"false"},"["),s("mn",null,"2"),s("mo",{stretchy:"false"},"]"),s("mo",{"data-mjx-texclass":"ORD",stretchy:"false"},"|"),s("mo",null,"≠"),s("mo",{"data-mjx-texclass":"ORD",stretchy:"false"},"|"),s("mi",null,"x"),s("mo",{stretchy:"false"},"["),s("mn",null,"3"),s("mo",{stretchy:"false"},"]"),s("mo",null,"−"),s("mi",null,"x"),s("mo",{stretchy:"false"},"["),s("mn",null,"4"),s("mo",{stretchy:"false"},"]"),s("mo",{"data-mjx-texclass":"ORD",stretchy:"false"},"|")])],-1),I=n(`
The constraint is then added to the usual constraints collection.
julia
const description_dist_different = """
-Ensures that the distances between marks on the ruler are unique.
-"""
-
-# Define the predicate
-predicate_dist_different(x) = abs(x[1] - x[2]) ≠ abs(x[3] - x[4])
-
-# Add it to usual constraints
-@usual concept_dist_different(x) = xcsp_intension(
-    list = x,
-    predicate = predicate_dist_different
-)
Please check the section dedicated to the Golomb Ruler problem to see a use for this constraint. <!– TODO: Golomb Ruler –>
APIs ​
Note that the intension constraint is not directly available through the JC-API in Constraints.jl. It is designed as such since defining a constraint through a predicate is the natural way.
We provide here a usage example for the :dist_different constraint, previously added to the USUAL_CONSTRAINTS collection.
Higher level modeling language such as JuMP should provide an Intension interface.
julia
using Constraints
-
-concept(:dist_different, x)
-concept(:dist_different)(x)
julia
# Defines the DistDifferent constraint
-using Constraints
-
-c = x -> xcsp_intension(
-    list = x,
-    predicate = y -> abs(y[1] - y[2]) ≠ abs(y[3] - y[4])
-)
-
-c([1, 2, 3, 3]) # true
-c([1, 2, 3, 4]) # false
julia
using CBLS, JuMP
-
-model = Model(CBLS.Optimizer)
-@variable(model, 0 <= X[1:4] <= 10, Int)
-@constraint(model, X in DistDifferent())
-optimize!(model)
-
-@info value.(X)
-
-# Note that this example gives a solution for the constraint within the interval 0:10
julia
# TODO: How to handle intention in JuMP/MOI
Specific documentation ​
# Constraints.xcsp_intension — Function. 
julia
xcsp_intension(list, predicate)
An intensional constraint is usually defined from a predicate over list. As such it encompass any generic constraint.
Arguments
  • list::Vector{Int}: A list of variables
  • predicate::Function: A predicate over list
Variants
  • :dist_different: A constraint ensuring that the distances between marks on the ruler are unique. Specifically, it checks that the distance between x[1] and x[2], and the distance between x[3] and x[4], are different. This constraint is fundamental in ensuring the validity of a Golomb ruler, where no two pairs of marks should have the same distance between them.
julia
concept(:dist_different, x)
-concept(:dist_different)(x)
Examples
@example
2 + 2
@example
2 + 2
@example
using Constraints # hide
-c = concept(:dist_different)
-c([1, 2, 3, 3]) && !c([1, 2, 3, 4])
@example
using Constraints # hide
-c = concept(:dist_different)
-c([1, 2, 3, 3]) && !c([1, 2, 3, 4])
source

Extension Constraints ​
These are constraints that are defined by explicitly listing all the tuples of values that satisfy the constraint. They are called extensional because they are defined by the set of values they allow. For example, a binary constraint that specifies that a variable X must be either 1 or 2 and a variable Y must be either 3 or 4 could be defined extensionally by the set of tuples {(1,3), (1,4), (2,3), (2,4)}.
These two types of constraints provide a flexible way to define complex relationships between variables in constraint programming.
XCSP in Constraints.jl {#XCSP-in-Constraints.jl} ​
# Constraints.xcsp_extension — Function. 
julia
xcsp_extension(; list, supports=nothing, conflicts=nothing)
Global constraint enforcing that the tuple x matches a configuration within the supports set pair_vars[1] or does not match any configuration within the conflicts set pair_vars[2]. It embodies the logic: x ∈ pair_vars[1] || x ∉ pair_vars[2], providing a comprehensive way to define valid (supported) and invalid (conflicted) tuples for constraint satisfaction problems. This constraint is versatile, allowing for the explicit delineation of both acceptable and unacceptable configurations.
Arguments
  • list::Vector{Int}: A list of variables
  • supports::Vector{Vector{Int}}: A set of supported tuples. Default to nothing.
  • conflicts::Vector{Vector{Int}}: A set of conflicted tuples. Default to nothing.
Variants
  • :extension: Global constraint enforcing that the tuple x matches a configuration within the supports set pair_vars[1] or does not match any configuration within the conflicts set pair_vars[2]. It embodies the logic: x ∈ pair_vars[1] || x ∉ pair_vars[2], providing a comprehensive way to define valid (supported) and invalid (conflicted) tuples for constraint satisfaction problems. This constraint is versatile, allowing for the explicit delineation of both acceptable and unacceptable configurations.
julia
concept(:extension, x; pair_vars)
-concept(:extension)(x; pair_vars)
  • :supports: Global constraint ensuring that the tuple x matches a configuration listed within the support set pair_vars. This constraint is derived from the extension model, specifying that x must be one of the explicitly defined supported configurations: x ∈ pair_vars. It is utilized to directly declare the tuples that are valid and should be included in the solution space.
julia
concept(:supports, x; pair_vars)
-concept(:supports)(x; pair_vars)
  • :conflicts: Global constraint ensuring that the tuple x does not match any configuration listed within the conflict set pair_vars. This constraint, originating from the extension model, stipulates that x must avoid all configurations defined as conflicts: x ∉ pair_vars. It is useful for specifying tuples that are explicitly forbidden and should be excluded from the solution space.
julia
concept(:conflicts, x; pair_vars)
-concept(:conflicts)(x; pair_vars)
Examples
julia
c = concept(:extension)
-c([1, 2, 3, 4, 5]; pair_vars=[[1, 2, 3, 4, 5]])
-c([1, 2, 3, 4, 5]; pair_vars=([[1, 2, 3, 4, 5]], [[1, 2, 1, 4, 5], [1, 2, 3, 5, 5]]))
-c([1, 2, 3, 4, 5]; pair_vars=[[1, 2, 1, 4, 5], [1, 2, 3, 5, 5]])
-
-c = concept(:supports)
-c([1, 2, 3, 4, 5]; pair_vars=[[1, 2, 3, 4, 5]])
-
-c = concept(:conflicts)
-c([1, 2, 3, 4, 5]; pair_vars=[[1, 2, 1, 4, 5], [1, 2, 3, 5, 5]])
source

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diff --git a/previews/PR53/assets/constraints_generic_constraints.md.CtmFhNuA.lean.js b/previews/PR53/assets/constraints_generic_constraints.md.CtmFhNuA.lean.js
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index 99137c1..0000000
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+++ /dev/null
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-import{_ as e,c as a,j as s,a as i,a7 as n,o as t}from"./chunks/framework.B__MqT43.js";const O=JSON.parse('{"title":"Generic Constraints","description":"","frontmatter":{},"headers":[],"relativePath":"constraints/generic_constraints.md","filePath":"constraints/generic_constraints.md","lastUpdated":null}'),l={name:"constraints/generic_constraints.md"},h=n("",3),p={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},k={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.025ex"},xmlns:"http://www.w3.org/2000/svg",width:"1.294ex",height:"1.025ex",role:"img",focusable:"false",viewBox:"0 -442 572 453","aria-hidden":"true"},r=s("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[s("g",{"data-mml-node":"math"},[s("g",{"data-mml-node":"mi"},[s("path",{"data-c":"1D465",d:"M52 289Q59 331 106 386T222 442Q257 442 286 424T329 379Q371 442 430 442Q467 442 494 420T522 361Q522 332 508 314T481 292T458 288Q439 288 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0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"x")])],-1),c={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},E={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.464ex"},xmlns:"http://www.w3.org/2000/svg",width:"1.109ex",height:"1.464ex",role:"img",focusable:"false",viewBox:"0 -442 490 647","aria-hidden":"true"},g=s("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[s("g",{"data-mml-node":"math"},[s("g",{"data-mml-node":"mi"},[s("path",{"data-c":"1D466",d:"M21 287Q21 301 36 335T84 406T158 442Q199 442 224 419T250 355Q248 336 247 334Q247 331 231 288T198 191T182 105Q182 62 196 45T238 27Q261 27 281 38T312 61T339 94Q339 95 344 114T358 173T377 247Q415 397 419 404Q432 431 462 431Q475 431 483 424T494 412T496 403Q496 390 447 193T391 -23Q363 -106 294 -155T156 -205Q111 -205 77 -183T43 -117Q43 -95 50 -80T69 -58T89 -48T106 -45Q150 -45 150 -87Q150 -107 138 -122T115 -142T102 -147L99 -148Q101 -153 118 -160T152 -167H160Q177 -167 186 -165Q219 -156 247 -127T290 -65T313 -9T321 21L315 17Q309 13 296 6T270 -6Q250 -11 231 -11Q185 -11 150 11T104 82Q103 89 103 113Q103 170 138 262T173 379Q173 380 173 381Q173 390 173 393T169 400T158 404H154Q131 404 112 385T82 344T65 302T57 280Q55 278 41 278H27Q21 284 21 287Z",style:{"stroke-width":"3"}})])])],-1),y=[g],C=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"y")])],-1),u={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},Q={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.464ex"},xmlns:"http://www.w3.org/2000/svg",width:"5.42ex",height:"1.686ex",role:"img",focusable:"false",viewBox:"0 -540 2395.6 745","aria-hidden":"true"},m=n("",1),T=[m],F=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"x"),s("mo",null,"<"),s("mi",null,"y")])],-1),x=n("",4),b=s("code",null,"dist_different",-1),f=s("em",null,"Constraints.jl",-1),v=s("code",null,"dist_different",-1),_={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},B={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.025ex"},xmlns:"http://www.w3.org/2000/svg",width:"1.294ex",height:"1.025ex",role:"img",focusable:"false",viewBox:"0 -442 572 453","aria-hidden":"true"},w=s("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[s("g",{"data-mml-node":"math"},[s("g",{"data-mml-node":"mi"},[s("path",{"data-c":"1D465",d:"M52 289Q59 331 106 386T222 442Q257 442 286 424T329 379Q371 442 430 442Q467 442 494 420T522 361Q522 332 508 314T481 292T458 288Q439 288 427 299T415 328Q415 374 465 391Q454 404 425 404Q412 404 406 402Q368 386 350 336Q290 115 290 78Q290 50 306 38T341 26Q378 26 414 59T463 140Q466 150 469 151T485 153H489Q504 153 504 145Q504 144 502 134Q486 77 440 33T333 -11Q263 -11 227 52Q186 -10 133 -10H127Q78 -10 57 16T35 71Q35 103 54 123T99 143Q142 143 142 101Q142 81 130 66T107 46T94 41L91 40Q91 39 97 36T113 29T132 26Q168 26 194 71Q203 87 217 139T245 247T261 313Q266 340 266 352Q266 380 251 392T217 404Q177 404 142 372T93 290Q91 281 88 280T72 278H58Q52 284 52 289Z",style:{"stroke-width":"3"}})])])],-1),H=[w],A=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mi",null,"x")])],-1),D={class:"MathJax",jax:"SVG",display:"true",style:{direction:"ltr",display:"block","text-align":"center",margin:"1em 0",position:"relative"}},V={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.566ex"},xmlns:"http://www.w3.org/2000/svg",width:"25.797ex",height:"2.262ex",role:"img",focusable:"false",viewBox:"0 -750 11402.4 1000","aria-hidden":"true"},M=n("",1),j=[M],L=s("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[s("mo",{"data-mjx-texclass":"ORD",stretchy:"false"},"|"),s("mi",null,"x"),s("mo",{stretchy:"false"},"["),s("mn",null,"1"),s("mo",{stretchy:"false"},"]"),s("mo",null,"−"),s("mi",null,"x"),s("mo",{stretchy:"false"},"["),s("mn",null,"2"),s("mo",{stretchy:"false"},"]"),s("mo",{"data-mjx-texclass":"ORD",stretchy:"false"},"|"),s("mo",null,"≠"),s("mo",{"data-mjx-texclass":"ORD",stretchy:"false"},"|"),s("mi",null,"x"),s("mo",{stretchy:"false"},"["),s("mn",null,"3"),s("mo",{stretchy:"false"},"]"),s("mo",null,"−"),s("mi",null,"x"),s("mo",{stretchy:"false"},"["),s("mn",null,"4"),s("mo",{stretchy:"false"},"]"),s("mo",{"data-mjx-texclass":"ORD",stretchy:"false"},"|")])],-1),I=n("",17);function P(S,Z,q,G,J,R){return t(),a("div",null,[h,s("p",null,[i("These are constraints that are defined by a logical expression or a function. They are called intentional because they are defined by the property they satisfy. For example, a constraint that specifies that a variable "),s("mjx-container",p,[(t(),a("svg",k,d)),o]),i(" must be less than a variable "),s("mjx-container",c,[(t(),a("svg",E,y)),C]),i(" could be defined intentionally as "),s("mjx-container",u,[(t(),a("svg",Q,T)),F]),i(".")]),x,s("p",null,[i("We use the "),b,i(" constraint to illustrate how to define an intention constraint in "),f,i(". The "),v,i(" constraint ensures that the distances between marks "),s("mjx-container",_,[(t(),a("svg",B,H)),A]),i(" on a ruler are unique.")]),s("mjx-container",D,[(t(),a("svg",V,j)),L]),I])}const X=e(l,[["render",P]]);export{O as __pageData,X as default};
diff --git a/previews/PR53/assets/constraints_packing_scheduling_constraints.md.xDi-PHIu.js b/previews/PR53/assets/constraints_packing_scheduling_constraints.md.6H7cStQm.js
similarity index 98%
rename from previews/PR53/assets/constraints_packing_scheduling_constraints.md.xDi-PHIu.js
rename to previews/PR53/assets/constraints_packing_scheduling_constraints.md.6H7cStQm.js
index d1241e1..102ae68 100644
--- a/previews/PR53/assets/constraints_packing_scheduling_constraints.md.xDi-PHIu.js
+++ b/previews/PR53/assets/constraints_packing_scheduling_constraints.md.6H7cStQm.js
@@ -1,4 +1,4 @@
-import{_ as s,c as i,o as a,a7 as h}from"./chunks/framework.B__MqT43.js";const F=JSON.parse('{"title":"Constraints.jl: Streamlining Constraint Definition and Integration in Julia","description":"","frontmatter":{},"headers":[],"relativePath":"constraints/packing_scheduling_constraints.md","filePath":"constraints/packing_scheduling_constraints.md","lastUpdated":null}'),k={name:"constraints/packing_scheduling_constraints.md"},n=h(`
Constraints.jl: Streamlining Constraint Definition and Integration in Julia ​
julia
using Constraints
+import{_ as s,c as i,o as a,a7 as h}from"./chunks/framework.B__MqT43.js";const F=JSON.parse('{"title":"Constraints.jl: Streamlining Constraint Definition and Integration in Julia","description":"","frontmatter":{},"headers":[],"relativePath":"constraints/packing_scheduling_constraints.md","filePath":"constraints/packing_scheduling_constraints.md","lastUpdated":null}'),k={name:"constraints/packing_scheduling_constraints.md"},n=h(`
Constraints.jl: Streamlining Constraint Definition and Integration in Julia ​
julia
using Constraints
 
 @info concept(:cumulative, [1, 2, 3, 4, 5]; val = 1)
 @info concept(:cumulative, [1, 2, 2, 4, 5]; val = 1)
@@ -25,7 +25,7 @@ import{_ as s,c as i,o as a,a7 as h}from"./chunks/framework.B__MqT43.js";const F
 @constraint(model,
     Z in Cumulative(; pair_vars = [3 2 5 4 2; 1 2 1 1 3], op = <, val = 5))
 JuMP.optimize!(model)
-@info "Cumulative" value.(X) value.(Y) value.(Z)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
+@info "Cumulative" value.(X) value.(Y) value.(Z)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
 
 @info concept(:no_overlap, [1, 2, 3, 4, 5])
 @info concept(:no_overlap, [1, 2, 3, 4, 1])
diff --git a/previews/PR53/assets/constraints_packing_scheduling_constraints.md.xDi-PHIu.lean.js b/previews/PR53/assets/constraints_packing_scheduling_constraints.md.6H7cStQm.lean.js
similarity index 100%
rename from previews/PR53/assets/constraints_packing_scheduling_constraints.md.xDi-PHIu.lean.js
rename to previews/PR53/assets/constraints_packing_scheduling_constraints.md.6H7cStQm.lean.js
diff --git a/previews/PR53/assets/constraints_variables_and_domains.md.BX9WpOqJ.js b/previews/PR53/assets/constraints_variables_and_domains.md.hAN5u75S.js
similarity index 95%
rename from previews/PR53/assets/constraints_variables_and_domains.md.BX9WpOqJ.js
rename to previews/PR53/assets/constraints_variables_and_domains.md.hAN5u75S.js
index f838b37..69f471d 100644
--- a/previews/PR53/assets/constraints_variables_and_domains.md.BX9WpOqJ.js
+++ b/previews/PR53/assets/constraints_variables_and_domains.md.hAN5u75S.js
@@ -1,4 +1,4 @@
-import{_ as e,c as a,j as s,a as i,a7 as t,o as n}from"./chunks/framework.B__MqT43.js";const f=JSON.parse('{"title":"Defining Variables and Exploring Domains","description":"","frontmatter":{},"headers":[],"relativePath":"constraints/variables_and_domains.md","filePath":"constraints/variables_and_domains.md","lastUpdated":null}'),l={name:"constraints/variables_and_domains.md"},h=t('
Defining Variables and Exploring Domains ​
ConstraintDomains.jl stands as the standard way to define variables and explore domains within the Julia Constraints ecosystem. This package provides the infrastructure necessary for specifying both discrete and continuous domains. Explorations features are mainly related to the learning about constraints aspect and will be detailed in that chapter.
Variables and their domains can also be defined through MOI and JuMP syntaxes in their respective models.
Implementing the AbstractDomain Interface ​
',4),p=s("em",null,"ConstraintDomains.jl",-1),k=s("code",null,"AbstractDomain",-1),d=s("code",null,"AbstractDomain",-1),r={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},o={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.09ex"},xmlns:"http://www.w3.org/2000/svg",width:"1.509ex",height:"1.312ex",role:"img",focusable:"false",viewBox:"0 -540 667 580","aria-hidden":"true"},E=s("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[s("g",{"data-mml-node":"math"},[s("g",{"data-mml-node":"mo"},[s("path",{"data-c":"2208",d:"M84 250Q84 372 166 450T360 539Q361 539 377 539T419 540T469 540H568Q583 532 583 520Q583 511 570 501L466 500Q355 499 329 494Q280 482 242 458T183 409T147 354T129 306T124 272V270H568Q583 262 583 250T568 230H124V228Q124 207 134 177T167 112T231 48T328 7Q355 1 466 0H570Q583 -10 583 -20Q583 -32 568 -40H471Q464 -40 446 -40T417 -41Q262 -41 172 45Q84 127 84 250Z",style:{"stroke-width":"3"}})])])],-1),g=[E],c=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mo",null,"∈")])],-1),y=s("code",null,"rand",-1),m=s("code",null,"length",-1),C=t(`
# ConstraintDomains.AbstractDomain — Type. 
julia
AbstractDomain
An abstract super type for any domain type. A domain type D <: AbstractDomain must implement the following methods to properly interface AbstractDomain.
  • Base.∈(val, ::D)
  • Base.rand(::D)
  • Base.length(::D) that is the number of elements in a discrete domain, and the distance between bounds or similar for a continuous domain
Additionally, if the domain is used in a dynamic context, it can extend
  • add!(::D, args)
  • delete!(::D, args)
where args depends on D's structure
source

Discrete Domains ​
Optimization in discrete spaces has been the core of Constraint Programming since its inception. We provide three kinds of discrete domains.
SetDomain ​
A SetDomain is simply a Set of unordered numerical values.
julia
using ConstraintDomains
+import{_ as e,c as a,j as s,a as i,a7 as t,o as n}from"./chunks/framework.B__MqT43.js";const _=JSON.parse('{"title":"Defining Variables and Exploring Domains","description":"","frontmatter":{},"headers":[],"relativePath":"constraints/variables_and_domains.md","filePath":"constraints/variables_and_domains.md","lastUpdated":null}'),l={name:"constraints/variables_and_domains.md"},h=t('
Defining Variables and Exploring Domains ​
ConstraintDomains.jl stands as the standard way to define variables and explore domains within the Julia Constraints ecosystem. This package provides the infrastructure necessary for specifying both discrete and continuous domains. Explorations features are mainly related to the learning about constraints aspect and will be detailed in that chapter.
Variables and their domains can also be defined through MOI and JuMP syntaxes in their respective models.
Implementing the AbstractDomain Interface ​
',4),p=s("em",null,"ConstraintDomains.jl",-1),k=s("code",null,"AbstractDomain",-1),r=s("code",null,"AbstractDomain",-1),d={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},o={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.09ex"},xmlns:"http://www.w3.org/2000/svg",width:"1.509ex",height:"1.312ex",role:"img",focusable:"false",viewBox:"0 -540 667 580","aria-hidden":"true"},E=s("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[s("g",{"data-mml-node":"math"},[s("g",{"data-mml-node":"mo"},[s("path",{"data-c":"2208",d:"M84 250Q84 372 166 450T360 539Q361 539 377 539T419 540T469 540H568Q583 532 583 520Q583 511 570 501L466 500Q355 499 329 494Q280 482 242 458T183 409T147 354T129 306T124 272V270H568Q583 262 583 250T568 230H124V228Q124 207 134 177T167 112T231 48T328 7Q355 1 466 0H570Q583 -10 583 -20Q583 -32 568 -40H471Q464 -40 446 -40T417 -41Q262 -41 172 45Q84 127 84 250Z",style:{"stroke-width":"3"}})])])],-1),g=[E],c=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mo",null,"∈")])],-1),y=s("code",null,"rand",-1),m=s("code",null,"length",-1),C=t(`
# ConstraintDomains.AbstractDomain — Type. 
julia
AbstractDomain
An abstract super type for any domain type. A domain type D <: AbstractDomain must implement the following methods to properly interface AbstractDomain.
  • Base.∈(val, ::D)
  • Base.rand(::D)
  • Base.length(::D) that is the number of elements in a discrete domain, and the distance between bounds or similar for a continuous domain
Additionally, if the domain is used in a dynamic context, it can extend
  • add!(::D, args)
  • delete!(::D, args)
where args depends on D's structure
source

Discrete Domains ​
Optimization in discrete spaces has been the core of Constraint Programming since its inception. We provide three kinds of discrete domains.
SetDomain ​
A SetDomain is simply a Set of unordered numerical values.
julia
using ConstraintDomains
 
 d1 = domain([53.69, 89.2, 0.12])
 d2 = domain([2//3, 89//123])
@@ -25,7 +25,7 @@ import{_ as e,c as a,j as s,a as i,a7 as t,o as n}from"./chunks/framework.B__MqT
 MOI.add_constraint(optimizer, v3, CBLS.DiscreteSet(4.3))
 
 v4 = MOI.add_variable(optimizer)
-MOI.add_constraint(optimizer, v4, CBLS.DiscreteSet([1, 42, 3.14]))
RangeDomain ​
A range domain allows for minimal storage and more efficient operation on discrete sets defined as Range in Julia. It is not recommended for dynamic domains (it will be replaced with SetDomain as soon as a non-extremal element is removed).
julia
using ConstraintDomains
+MOI.add_constraint(optimizer, v4, CBLS.DiscreteSet([1, 42, 3.14]))
RangeDomain ​
A range domain allows for minimal storage and more efficient operation on discrete sets defined as Range in Julia. It is not recommended for dynamic domains (it will be replaced with SetDomain as soon as a non-extremal element is removed).
julia
using ConstraintDomains
 
 d1 = domain(1:5)
 d2 = domain(0.4:0.1:1.3)
julia
## To be implemented
@@ -45,8 +45,8 @@ import{_ as e,c as a,j as s,a as i,a7 as t,o as n}from"./chunks/framework.B__MqT
 
 # v2 = MOI.add_variable(optimizer)
 
-# MOI.add_constraint(optimizer, v1, MOI.RangeSet(0.4:0.1:1.3))
Arbitrary Domains ​
As odd as it may sound, we provide a constructor for sets of elements making up arbitrary, possibly non-numerical, domains.
Until some practical examples are implemented, this structure will mainly be a placeholder with default behavior.
Continuous Domains ​
Numerous problems cannot be challenged without expressing at least part of their domains as continuous variables. In Julia Constraints we provide such domains as (set of) intervals.
julia
using ConstraintDomains, Intervals
+# MOI.add_constraint(optimizer, v1, MOI.RangeSet(0.4:0.1:1.3))
Arbitrary Domains ​
As odd as it may sound, we provide a constructor for sets of elements making up arbitrary, possibly non-numerical, domains.
Until some practical examples are implemented, this structure will mainly be a placeholder with default behavior.
Continuous Domains ​
Numerous problems cannot be challenged without expressing at least part of their domains as continuous variables. In Julia Constraints we provide such domains as (set of) intervals.
julia
using ConstraintDomains, Intervals
 
 d1 = domain(Interval{Open,Closed}(3.2, true), (42, false))
 d2 = domain(3.2..42)
-d3 = domain([3.2..42, 63.2..324.1])
julia
## see MOI.Interval
julia
## see MOI.Interval
`,16);function F(u,b,D,v,A,B){return n(),a("div",null,[h,s("p",null,[i("At the foundation of "),p,i(" is the "),k,i(" type, an abstract supertype for all domain types. Implementations of "),d,i(" must provide methods for checking membership ("),s("mjx-container",r,[(n(),a("svg",o,g)),c]),i("), generating random elements ("),y,i("), and determining the domain's size or range ("),m,i("). These functionalities are essential for defining the behavior and properties of variable domains within constraint models.")]),C])}const x=e(l,[["render",F]]);export{f as __pageData,x as default};
+d3 = domain([3.2..42, 63.2..324.1])
julia
## see MOI.Interval
julia
## see MOI.Interval
`,16);function F(u,b,D,v,A,B){return n(),a("div",null,[h,s("p",null,[i("At the foundation of "),p,i(" is the "),k,i(" type, an abstract supertype for all domain types. Implementations of "),r,i(" must provide methods for checking membership ("),s("mjx-container",d,[(n(),a("svg",o,g)),c]),i("), generating random elements ("),y,i("), and determining the domain's size or range ("),m,i("). These functionalities are essential for defining the behavior and properties of variable domains within constraint models.")]),C])}const x=e(l,[["render",F]]);export{_ as __pageData,x as default};
diff --git a/previews/PR53/assets/constraints_variables_and_domains.md.BX9WpOqJ.lean.js b/previews/PR53/assets/constraints_variables_and_domains.md.hAN5u75S.lean.js
similarity index 87%
rename from previews/PR53/assets/constraints_variables_and_domains.md.BX9WpOqJ.lean.js
rename to previews/PR53/assets/constraints_variables_and_domains.md.hAN5u75S.lean.js
index c3a7d0b..c1b6969 100644
--- a/previews/PR53/assets/constraints_variables_and_domains.md.BX9WpOqJ.lean.js
+++ b/previews/PR53/assets/constraints_variables_and_domains.md.hAN5u75S.lean.js
@@ -1 +1 @@
-import{_ as e,c as a,j as s,a as i,a7 as t,o as n}from"./chunks/framework.B__MqT43.js";const f=JSON.parse('{"title":"Defining Variables and Exploring Domains","description":"","frontmatter":{},"headers":[],"relativePath":"constraints/variables_and_domains.md","filePath":"constraints/variables_and_domains.md","lastUpdated":null}'),l={name:"constraints/variables_and_domains.md"},h=t("",4),p=s("em",null,"ConstraintDomains.jl",-1),k=s("code",null,"AbstractDomain",-1),d=s("code",null,"AbstractDomain",-1),r={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},o={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.09ex"},xmlns:"http://www.w3.org/2000/svg",width:"1.509ex",height:"1.312ex",role:"img",focusable:"false",viewBox:"0 -540 667 580","aria-hidden":"true"},E=s("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[s("g",{"data-mml-node":"math"},[s("g",{"data-mml-node":"mo"},[s("path",{"data-c":"2208",d:"M84 250Q84 372 166 450T360 539Q361 539 377 539T419 540T469 540H568Q583 532 583 520Q583 511 570 501L466 500Q355 499 329 494Q280 482 242 458T183 409T147 354T129 306T124 272V270H568Q583 262 583 250T568 230H124V228Q124 207 134 177T167 112T231 48T328 7Q355 1 466 0H570Q583 -10 583 -20Q583 -32 568 -40H471Q464 -40 446 -40T417 -41Q262 -41 172 45Q84 127 84 250Z",style:{"stroke-width":"3"}})])])],-1),g=[E],c=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mo",null,"∈")])],-1),y=s("code",null,"rand",-1),m=s("code",null,"length",-1),C=t("",16);function F(u,b,D,v,A,B){return n(),a("div",null,[h,s("p",null,[i("At the foundation of "),p,i(" is the "),k,i(" type, an abstract supertype for all domain types. Implementations of "),d,i(" must provide methods for checking membership ("),s("mjx-container",r,[(n(),a("svg",o,g)),c]),i("), generating random elements ("),y,i("), and determining the domain's size or range ("),m,i("). These functionalities are essential for defining the behavior and properties of variable domains within constraint models.")]),C])}const x=e(l,[["render",F]]);export{f as __pageData,x as default};
+import{_ as e,c as a,j as s,a as i,a7 as t,o as n}from"./chunks/framework.B__MqT43.js";const _=JSON.parse('{"title":"Defining Variables and Exploring Domains","description":"","frontmatter":{},"headers":[],"relativePath":"constraints/variables_and_domains.md","filePath":"constraints/variables_and_domains.md","lastUpdated":null}'),l={name:"constraints/variables_and_domains.md"},h=t("",4),p=s("em",null,"ConstraintDomains.jl",-1),k=s("code",null,"AbstractDomain",-1),r=s("code",null,"AbstractDomain",-1),d={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},o={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.09ex"},xmlns:"http://www.w3.org/2000/svg",width:"1.509ex",height:"1.312ex",role:"img",focusable:"false",viewBox:"0 -540 667 580","aria-hidden":"true"},E=s("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[s("g",{"data-mml-node":"math"},[s("g",{"data-mml-node":"mo"},[s("path",{"data-c":"2208",d:"M84 250Q84 372 166 450T360 539Q361 539 377 539T419 540T469 540H568Q583 532 583 520Q583 511 570 501L466 500Q355 499 329 494Q280 482 242 458T183 409T147 354T129 306T124 272V270H568Q583 262 583 250T568 230H124V228Q124 207 134 177T167 112T231 48T328 7Q355 1 466 0H570Q583 -10 583 -20Q583 -32 568 -40H471Q464 -40 446 -40T417 -41Q262 -41 172 45Q84 127 84 250Z",style:{"stroke-width":"3"}})])])],-1),g=[E],c=s("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[s("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[s("mo",null,"∈")])],-1),y=s("code",null,"rand",-1),m=s("code",null,"length",-1),C=t("",16);function F(u,b,D,v,A,B){return n(),a("div",null,[h,s("p",null,[i("At the foundation of "),p,i(" is the "),k,i(" type, an abstract supertype for all domain types. Implementations of "),r,i(" must provide methods for checking membership ("),s("mjx-container",d,[(n(),a("svg",o,g)),c]),i("), generating random elements ("),y,i("), and determining the domain's size or range ("),m,i("). These functionalities are essential for defining the behavior and properties of variable domains within constraint models.")]),C])}const x=e(l,[["render",F]]);export{_ as __pageData,x as default};
diff --git a/previews/PR53/assets/cp_getting_started.md.Bp6ZBR6j.js b/previews/PR53/assets/cp_getting_started.md.CtxbRJ9l.js
similarity index 94%
rename from previews/PR53/assets/cp_getting_started.md.Bp6ZBR6j.js
rename to previews/PR53/assets/cp_getting_started.md.CtxbRJ9l.js
index 12408b4..a9e7ac2 100644
--- a/previews/PR53/assets/cp_getting_started.md.Bp6ZBR6j.js
+++ b/previews/PR53/assets/cp_getting_started.md.CtxbRJ9l.js
@@ -1,9 +1,9 @@
-import{_ as l,c as t,j as i,a as s,a7 as a,o as e}from"./chunks/framework.B__MqT43.js";const H=JSON.parse('{"title":"Getting Started with Julia for CP and Optimization","description":"","frontmatter":{},"headers":[],"relativePath":"cp/getting_started.md","filePath":"cp/getting_started.md","lastUpdated":null}'),n={name:"cp/getting_started.md"},h=a('
Getting Started with Julia for CP and Optimization ​
Why Julia? ​
  • Discuss the advantages of Julia for computational science and optimization, highlighting its performance and ease of use.
Setting Up Your Julia Environment ​
We encourage users to install Julia through juliaup, a version manager for the Julia language. Please look at the official Julia language download page for further information. Once installed, Julia can be used through various editors (Visual Studio Code), notebooks (Pluto.jl), or command-line (REPL).
Although a part of the CP solvers available within the Julia ecosystem have their own interface, we encourage users to use the JuMP modeling language if possible.
Julia Constraints host several solvers(' interfaces). Due to its flexibility in modeling and solving, we will use LocalSearchSolvers.jl through its JuMP interface CBLS.jl as the basic example. Note that depending on the targeted instances, available hardware, and expectations, it is not necessarily the best choice.
All along the documentation, we will try to provide syntax examples for different setup.
julia
using LocalSearchSolvers
julia
using JuMP, CBLS
julia
# TODO: Add other solvers
Your First Julia CP Model ​
We will start with a classic puzzle game and some of its not that simple variants: the Sudoku.
(From Wikipedia) In classic Sudoku, the objective is to fill a 9 × 9 grid with digits so that each column, each row, and each of the nine 3 × 3 subgrids that compose the grid (also called "boxes", "blocks", or "regions") contains all of the digits from 1 to 9. The puzzle setter provides a partially completed grid, which for a well-posed puzzle has a single solution.
Constraint Programming follows the model-and-solve approach. We first need to model our Sudoku problem.
julia
m = JuMP.Model(CBLS.Optimizer)
julia
# TODO: Add other solvers
But what are the basis of CP models? It is quite simple:
',15),o={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},p={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.439ex"},xmlns:"http://www.w3.org/2000/svg",width:"15.867ex",height:"1.984ex",role:"img",focusable:"false",viewBox:"0 -683 7013.4 877","aria-hidden":"true"},d=a('',1),r=[d],k=i("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[i("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[i("mi",null,"X"),i("mo",null,"="),i("msub",null,[i("mi",null,"X"),i("mn",null,"1")]),i("mo",null,","),i("mo",null,"⋯"),i("mo",null,","),i("msub",null,[i("mi",null,"X"),i("mi",null,"n")])])],-1),c=a('
julia
@variable(m, 1 ≤ X[1:9, 1:9] ≤ 9, Int)
julia
# TODO: Add other solvers
',1),Q={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},g={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.439ex"},xmlns:"http://www.w3.org/2000/svg",width:"15.148ex",height:"2.034ex",role:"img",focusable:"false",viewBox:"0 -705 6695.4 899","aria-hidden":"true"},T=a('',1),u=[T],m=i("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[i("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[i("mi",null,"C"),i("mo",null,"="),i("msub",null,[i("mi",null,"C"),i("mn",null,"1")]),i("mo",null,","),i("mo",null,"⋯"),i("mo",null,","),i("msub",null,[i("mi",null,"C"),i("mi",null,"n")])])],-1),v={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},y={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"0"},xmlns:"http://www.w3.org/2000/svg",width:"1.928ex",height:"1.545ex",role:"img",focusable:"false",viewBox:"0 -683 852 683","aria-hidden":"true"},b=i("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[i("g",{"data-mml-node":"math"},[i("g",{"data-mml-node":"mi"},[i("path",{"data-c":"1D44B",d:"M42 0H40Q26 0 26 11Q26 15 29 27Q33 41 36 43T55 46Q141 49 190 98Q200 108 306 224T411 342Q302 620 297 625Q288 636 234 637H206Q200 643 200 645T202 664Q206 677 212 683H226Q260 681 347 681Q380 681 408 681T453 682T473 682Q490 682 490 671Q490 670 488 658Q484 643 481 640T465 637Q434 634 411 620L488 426L541 485Q646 598 646 610Q646 628 622 635Q617 635 609 637Q594 637 594 648Q594 650 596 664Q600 677 606 683H618Q619 683 643 683T697 681T738 680Q828 680 837 683H845Q852 676 852 672Q850 647 840 637H824Q790 636 763 628T722 611T698 593L687 584Q687 585 592 480L505 384Q505 383 536 304T601 142T638 56Q648 47 699 46Q734 46 734 37Q734 35 732 23Q728 7 725 4T711 1Q708 1 678 1T589 2Q528 2 496 2T461 1Q444 1 444 10Q444 11 446 25Q448 35 450 39T455 44T464 46T480 47T506 54Q523 62 523 64Q522 64 476 181L429 299Q241 95 236 84Q232 76 232 72Q232 53 261 47Q262 47 267 47T273 46Q276 46 277 46T280 45T283 42T284 35Q284 26 282 19Q279 6 276 4T261 1Q258 1 243 1T201 2T142 2Q64 2 42 0Z",style:{"stroke-width":"3"}})])])],-1),E=[b],C=i("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[i("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[i("mi",null,"X")])],-1),f=a(`
When modeling problems as CP, one might define and use their own predicates. However, a large collection of already defined constraints exists. One, if not the most, iconic global constraint is called AllDifferent. It ensures that all variables take distinct values.
Sudoku puzzles can be defined using only this one constraint applied to different subsets of variables.
julia
for i in 1:9
+import{_ as l,c as t,j as i,a as s,a7 as a,o as e}from"./chunks/framework.B__MqT43.js";const H=JSON.parse('{"title":"Getting Started with Julia for CP and Optimization","description":"","frontmatter":{},"headers":[],"relativePath":"cp/getting_started.md","filePath":"cp/getting_started.md","lastUpdated":null}'),n={name:"cp/getting_started.md"},h=a('
Getting Started with Julia for CP and Optimization ​
Why Julia? ​
  • Discuss the advantages of Julia for computational science and optimization, highlighting its performance and ease of use.
Setting Up Your Julia Environment ​
We encourage users to install Julia through juliaup, a version manager for the Julia language. Please look at the official Julia language download page for further information. Once installed, Julia can be used through various editors (Visual Studio Code), notebooks (Pluto.jl), or command-line (REPL).
Although a part of the CP solvers available within the Julia ecosystem have their own interface, we encourage users to use the JuMP modeling language if possible.
Julia Constraints host several solvers(' interfaces). Due to its flexibility in modeling and solving, we will use LocalSearchSolvers.jl through its JuMP interface CBLS.jl as the basic example. Note that depending on the targeted instances, available hardware, and expectations, it is not necessarily the best choice.
All along the documentation, we will try to provide syntax examples for different setup.
julia
using LocalSearchSolvers
julia
using JuMP, CBLS
julia
# TODO: Add other solvers
Your First Julia CP Model ​
We will start with a classic puzzle game and some of its not that simple variants: the Sudoku.
(From Wikipedia) In classic Sudoku, the objective is to fill a 9 × 9 grid with digits so that each column, each row, and each of the nine 3 × 3 subgrids that compose the grid (also called "boxes", "blocks", or "regions") contains all of the digits from 1 to 9. The puzzle setter provides a partially completed grid, which for a well-posed puzzle has a single solution.
Constraint Programming follows the model-and-solve approach. We first need to model our Sudoku problem.
julia
m = JuMP.Model(CBLS.Optimizer)
julia
# TODO: Add other solvers
But what are the basis of CP models? It is quite simple:
',15),o={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},p={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.439ex"},xmlns:"http://www.w3.org/2000/svg",width:"15.867ex",height:"1.984ex",role:"img",focusable:"false",viewBox:"0 -683 7013.4 877","aria-hidden":"true"},d=a('',1),r=[d],k=i("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[i("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[i("mi",null,"X"),i("mo",null,"="),i("msub",null,[i("mi",null,"X"),i("mn",null,"1")]),i("mo",null,","),i("mo",null,"⋯"),i("mo",null,","),i("msub",null,[i("mi",null,"X"),i("mi",null,"n")])])],-1),c=a('
julia
@variable(m, 1 ≤ X[1:9, 1:9] ≤ 9, Int)
julia
# TODO: Add other solvers
',1),Q={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},g={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.439ex"},xmlns:"http://www.w3.org/2000/svg",width:"15.148ex",height:"2.034ex",role:"img",focusable:"false",viewBox:"0 -705 6695.4 899","aria-hidden":"true"},T=a('',1),u=[T],m=i("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[i("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[i("mi",null,"C"),i("mo",null,"="),i("msub",null,[i("mi",null,"C"),i("mn",null,"1")]),i("mo",null,","),i("mo",null,"⋯"),i("mo",null,","),i("msub",null,[i("mi",null,"C"),i("mi",null,"n")])])],-1),v={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},b={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"0"},xmlns:"http://www.w3.org/2000/svg",width:"1.928ex",height:"1.545ex",role:"img",focusable:"false",viewBox:"0 -683 852 683","aria-hidden":"true"},y=i("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[i("g",{"data-mml-node":"math"},[i("g",{"data-mml-node":"mi"},[i("path",{"data-c":"1D44B",d:"M42 0H40Q26 0 26 11Q26 15 29 27Q33 41 36 43T55 46Q141 49 190 98Q200 108 306 224T411 342Q302 620 297 625Q288 636 234 637H206Q200 643 200 645T202 664Q206 677 212 683H226Q260 681 347 681Q380 681 408 681T453 682T473 682Q490 682 490 671Q490 670 488 658Q484 643 481 640T465 637Q434 634 411 620L488 426L541 485Q646 598 646 610Q646 628 622 635Q617 635 609 637Q594 637 594 648Q594 650 596 664Q600 677 606 683H618Q619 683 643 683T697 681T738 680Q828 680 837 683H845Q852 676 852 672Q850 647 840 637H824Q790 636 763 628T722 611T698 593L687 584Q687 585 592 480L505 384Q505 383 536 304T601 142T638 56Q648 47 699 46Q734 46 734 37Q734 35 732 23Q728 7 725 4T711 1Q708 1 678 1T589 2Q528 2 496 2T461 1Q444 1 444 10Q444 11 446 25Q448 35 450 39T455 44T464 46T480 47T506 54Q523 62 523 64Q522 64 476 181L429 299Q241 95 236 84Q232 76 232 72Q232 53 261 47Q262 47 267 47T273 46Q276 46 277 46T280 45T283 42T284 35Q284 26 282 19Q279 6 276 4T261 1Q258 1 243 1T201 2T142 2Q64 2 42 0Z",style:{"stroke-width":"3"}})])])],-1),E=[y],C=i("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[i("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[i("mi",null,"X")])],-1),f=a(`
When modeling problems as CP, one might define and use their own predicates. However, a large collection of already defined constraints exists. One, if not the most, iconic global constraint is called AllDifferent. It ensures that all variables take distinct values.
Sudoku puzzles can be defined using only this one constraint applied to different subsets of variables.
julia
for i in 1:9
         @constraint(m, X[i,:] in AllDifferent()) # rows
         @constraint(m, X[:,i] in AllDifferent()) # columns
-end
julia
# TODO: Add other solvers
The last series of AllDifferent constraint is less straight forward. We need to ensure that each 3 × 3 subgrid (block) is filled with distinct values.
julia
for i in 0:2, j in 0:2 # blocks
+end
julia
# TODO: Add other solvers
The last series of AllDifferent constraint is less straight forward. We need to ensure that each 3 × 3 subgrid (block) is filled with distinct values.
julia
for i in 0:2, j in 0:2 # blocks
     @constraint(
         m,
         vec(X[(3i+1):(3(i+1)), (3j+1):(3(j+1))]) in AllDifferent(),
     )
-end
julia
# TODO: Add other solvers
We can now simply run our solver to look for a feasible solution.
julia
optimize!(m)
Note that this is heuristic solver, we might not get a feasible solution! Let's check it out. The value function print the value of a JuMP variable. We can cast it over a collection with the value. syntax.
julia
value.(X)
`,9);function _(F,w,x,D,A,L){return e(),t("div",null,[h,i("ol",null,[i("li",null,[s("A collection "),i("mjx-container",o,[(e(),t("svg",p,r)),k]),s(" of variables with each an associated domain.")])]),c,i("ol",null,[i("li",null,[s("A collection of predicates (called constraints) "),i("mjx-container",Q,[(e(),t("svg",g,u)),m]),s(" over (subsets of) "),i("mjx-container",v,[(e(),t("svg",y,E)),C]),s(".")])]),f])}const B=l(n,[["render",_]]);export{H as __pageData,B as default};
+end
julia
# TODO: Add other solvers
We can now simply run our solver to look for a feasible solution.
julia
optimize!(m)
Note that this is heuristic solver, we might not get a feasible solution! Let's check it out. The value function print the value of a JuMP variable. We can cast it over a collection with the value. syntax.
julia
value.(X)
`,9);function w(_,F,x,D,A,L){return e(),t("div",null,[h,i("ol",null,[i("li",null,[s("A collection "),i("mjx-container",o,[(e(),t("svg",p,r)),k]),s(" of variables with each an associated domain.")])]),c,i("ol",null,[i("li",null,[s("A collection of predicates (called constraints) "),i("mjx-container",Q,[(e(),t("svg",g,u)),m]),s(" over (subsets of) "),i("mjx-container",v,[(e(),t("svg",b,E)),C]),s(".")])]),f])}const S=l(n,[["render",w]]);export{H as __pageData,S as default};
diff --git a/previews/PR53/assets/cp_getting_started.md.Bp6ZBR6j.lean.js b/previews/PR53/assets/cp_getting_started.md.CtxbRJ9l.lean.js
similarity index 91%
rename from previews/PR53/assets/cp_getting_started.md.Bp6ZBR6j.lean.js
rename to previews/PR53/assets/cp_getting_started.md.CtxbRJ9l.lean.js
index 1ff98ec..88f35de 100644
--- a/previews/PR53/assets/cp_getting_started.md.Bp6ZBR6j.lean.js
+++ b/previews/PR53/assets/cp_getting_started.md.CtxbRJ9l.lean.js
@@ -1 +1 @@
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+import{_ as l,c as t,j as i,a as s,a7 as a,o as e}from"./chunks/framework.B__MqT43.js";const H=JSON.parse('{"title":"Getting Started with Julia for CP and Optimization","description":"","frontmatter":{},"headers":[],"relativePath":"cp/getting_started.md","filePath":"cp/getting_started.md","lastUpdated":null}'),n={name:"cp/getting_started.md"},h=a("",15),o={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},p={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.439ex"},xmlns:"http://www.w3.org/2000/svg",width:"15.867ex",height:"1.984ex",role:"img",focusable:"false",viewBox:"0 -683 7013.4 877","aria-hidden":"true"},d=a("",1),r=[d],k=i("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[i("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[i("mi",null,"X"),i("mo",null,"="),i("msub",null,[i("mi",null,"X"),i("mn",null,"1")]),i("mo",null,","),i("mo",null,"⋯"),i("mo",null,","),i("msub",null,[i("mi",null,"X"),i("mi",null,"n")])])],-1),c=a("",1),Q={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},g={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"-0.439ex"},xmlns:"http://www.w3.org/2000/svg",width:"15.148ex",height:"2.034ex",role:"img",focusable:"false",viewBox:"0 -705 6695.4 899","aria-hidden":"true"},T=a("",1),u=[T],m=i("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[i("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[i("mi",null,"C"),i("mo",null,"="),i("msub",null,[i("mi",null,"C"),i("mn",null,"1")]),i("mo",null,","),i("mo",null,"⋯"),i("mo",null,","),i("msub",null,[i("mi",null,"C"),i("mi",null,"n")])])],-1),v={class:"MathJax",jax:"SVG",style:{direction:"ltr",position:"relative"}},b={style:{overflow:"visible","min-height":"1px","min-width":"1px","vertical-align":"0"},xmlns:"http://www.w3.org/2000/svg",width:"1.928ex",height:"1.545ex",role:"img",focusable:"false",viewBox:"0 -683 852 683","aria-hidden":"true"},y=i("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[i("g",{"data-mml-node":"math"},[i("g",{"data-mml-node":"mi"},[i("path",{"data-c":"1D44B",d:"M42 0H40Q26 0 26 11Q26 15 29 27Q33 41 36 43T55 46Q141 49 190 98Q200 108 306 224T411 342Q302 620 297 625Q288 636 234 637H206Q200 643 200 645T202 664Q206 677 212 683H226Q260 681 347 681Q380 681 408 681T453 682T473 682Q490 682 490 671Q490 670 488 658Q484 643 481 640T465 637Q434 634 411 620L488 426L541 485Q646 598 646 610Q646 628 622 635Q617 635 609 637Q594 637 594 648Q594 650 596 664Q600 677 606 683H618Q619 683 643 683T697 681T738 680Q828 680 837 683H845Q852 676 852 672Q850 647 840 637H824Q790 636 763 628T722 611T698 593L687 584Q687 585 592 480L505 384Q505 383 536 304T601 142T638 56Q648 47 699 46Q734 46 734 37Q734 35 732 23Q728 7 725 4T711 1Q708 1 678 1T589 2Q528 2 496 2T461 1Q444 1 444 10Q444 11 446 25Q448 35 450 39T455 44T464 46T480 47T506 54Q523 62 523 64Q522 64 476 181L429 299Q241 95 236 84Q232 76 232 72Q232 53 261 47Q262 47 267 47T273 46Q276 46 277 46T280 45T283 42T284 35Q284 26 282 19Q279 6 276 4T261 1Q258 1 243 1T201 2T142 2Q64 2 42 0Z",style:{"stroke-width":"3"}})])])],-1),E=[y],C=i("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[i("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[i("mi",null,"X")])],-1),f=a("",9);function w(_,F,x,D,A,L){return e(),t("div",null,[h,i("ol",null,[i("li",null,[s("A collection "),i("mjx-container",o,[(e(),t("svg",p,r)),k]),s(" of variables with each an associated domain.")])]),c,i("ol",null,[i("li",null,[s("A collection of predicates (called constraints) "),i("mjx-container",Q,[(e(),t("svg",g,u)),m]),s(" over (subsets of) "),i("mjx-container",v,[(e(),t("svg",b,E)),C]),s(".")])]),f])}const S=l(n,[["render",w]]);export{H as __pageData,S as default};
diff --git a/previews/PR53/constraints/comparison_constraints.html b/previews/PR53/constraints/comparison_constraints.html
index a1249a8..1c41364 100644
--- a/previews/PR53/constraints/comparison_constraints.html
+++ b/previews/PR53/constraints/comparison_constraints.html
@@ -8,16 +8,16 @@
     
     
     
-    
+    
     
     
-    
-    
+    
+    
     
     
   
   
-    
Julia Constraints
Appearance
Constraints.jl: Streamlining Constraint Definition and Integration in Julia ​
julia
using Constraints
+    
Julia Constraints
Appearance
Constraints.jl: Streamlining Constraint Definition and Integration in Julia ​
julia
using Constraints
 
 concept(:all_different, [1,1,1,2]) # false
 concept(:all_different, [1,9,3,2]) # true
julia
using Constraints
@@ -36,7 +36,7 @@
 JuMP.optimize!(model)
 @info "All Different" value.(X) value.(Y)
 
-# Note that this example gives a solution for the all_different constraint.
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
+# Note that this example gives a solution for the all_different constraint.
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
 
 concept(:all_equal, [1,1,1,2]) #false
 concept(:all_equal, [1,1,1,1]) #true
julia
using Constraints
@@ -54,7 +54,7 @@
 JuMP.optimize!(model)
 @info "All Equal" value.(X)
 
-# Note that this example gives a solution for the all_equal constraint.
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
+# Note that this example gives a solution for the all_equal constraint.
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
 
 @info concept(:ordered, [1, 2, 3, 4, 4]; op=≤)
 @info concept(:ordered, [1, 2, 3, 3, 5]; op=<)
@@ -99,7 +99,7 @@
 c([1, 2, 3, 4, 5]; op=<)
 !c([1, 2, 3, 4, 3]; op=≤)
 !c([1, 2, 3, 4, 3]; op=<)
source


-    
+    
     
   
 
\ No newline at end of file
diff --git a/previews/PR53/constraints/connection_constraints.html b/previews/PR53/constraints/connection_constraints.html
index b147b64..8278fb9 100644
--- a/previews/PR53/constraints/connection_constraints.html
+++ b/previews/PR53/constraints/connection_constraints.html
@@ -8,16 +8,16 @@
     
     
     
-    
+    
     
     
-    
-    
+    
+    
     
     
   
   
-    
Julia Constraints
Appearance
Constraints.jl: Streamlining Constraint Definition and Integration in Julia ​
julia
using Constraints
+    
Julia Constraints
Appearance
Constraints.jl: Streamlining Constraint Definition and Integration in Julia ​
julia
using Constraints
 
 concept(:maximum, [1,1,1,2], val = 2, op = ==) # true
 concept(:maximum, [1,2,4,4], val = 2, op = ==) # false
julia
using Constraints
@@ -34,7 +34,7 @@
 @variable(model, 1≤X[1:5]≤5, Int)
 @constraint(model, X in Maximum(; op = ==, val = 5))
 optimize!(model)
-@info "Maximum" value.(X)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
+@info "Maximum" value.(X)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
 
 concept(:minimum, [1,1,1,2], val = 1, op = ==) # true
 concept(:minimum, [1,2,4,4], val = 2, op = ==) # false
julia
using Constraints
@@ -53,7 +53,7 @@
 JuMP.optimize!(model)
 @info "Minimum" value.(X)
 
-# Note that this example gives a solution for the minimum constraint.
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
+# Note that this example gives a solution for the minimum constraint.
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
 
 @info concept(:element, [1, 2, 3, 4, 5]; id=1, val=1)
 @info concept(:element, [1, 2, 3, 4, 5]; id=1, val=2)
@@ -77,7 +77,7 @@
 @constraint(model, Y in Element(; id = 1, val = 1))
 @constraint(model, Z in Element(; id = 2, val = 2))
 JuMP.optimize!(model)
-@info "Element" value.(X) value.(Y) value.(Z)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
+@info "Element" value.(X) value.(Y) value.(Z)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
 
 @info concept(:channel, [2, 1, 4, 3])
 @info concept(:channel, [1, 2, 3, 4])
@@ -126,7 +126,7 @@
 c([2, 1, 4, 3, 5, 2, 1, 4, 5, 3]; dim=2)
 c([false, false, true, false]; id=3)
 c([false, false, true, false]; id=1)
source


-    
+    
     
   
 
\ No newline at end of file
diff --git a/previews/PR53/constraints/constraint_commons.html b/previews/PR53/constraints/constraint_commons.html
index 3928580..3efe4f6 100644
--- a/previews/PR53/constraints/constraint_commons.html
+++ b/previews/PR53/constraints/constraint_commons.html
@@ -8,10 +8,10 @@
     
     
     
-    
+    
     
     
-    
+    
     
     
     
@@ -27,7 +27,7 @@
     :val, # one scalar value
     :vals, # a list of scalar values (independent of the input vector size)
 ]
source

# ConstraintCommons.extract_parameters — Function. 
julia
extract_parameters(m::Union{Method, Function}; parameters)
Extracts the intersection between the kargs of m and parameters (defaults to USUAL_CONSTRAINT_PARAMETERS).
source
julia
extract_parameters(s::Symbol, constraints_dict=USUAL_CONSTRAINTS; parameters=ConstraintCommons.USUAL_CONSTRAINT_PARAMETERS)
Return the parameters of the constraint s in constraints_dict.
Arguments
  • s::Symbol: the constraint name.
  • constraints_dict::Dict{Symbol,Constraint}: dictionary of constraints. Default is USUAL_CONSTRAINTS.
  • parameters::Vector{Symbol}: vector of parameters. Default is ConstraintCommons.USUAL_CONSTRAINT_PARAMETERS.
Example
julia
extract_parameters(:all_different)
source

Performances ​
Bench Evolution ParametersChair Evolution Parameters
Languages ​
XCSP3 considers two kinds of structure to recognize languages as core constraints: Automata, Multivalued Decision Diagrams (MMDs).
# ConstraintCommons.AbstractMultivaluedDecisionDiagram — Type. 
julia
AbstractMultivaluedDecisionDiagram
An abstract interface for Multivalued Decision Diagrams (MDD) used in Julia Constraints packages. Requirements:
  • accept(a<:AbstractMultivaluedDecisionDiagram, word): return true if a accepts word.
source

# ConstraintCommons.MDD — Type. 
julia
MDD{S,T} <: AbstractMultivaluedDecisionDiagram
A minimal implementation of a multivalued decision diagram structure.
source

# ConstraintCommons.AbstractAutomaton — Type. 
julia
AbstractAutomaton
An abstract interface for automata used in Julia Constraints packages. Requirements:
  • accept(a<:AbstractAutomaton, word): return true if a accepts word.
source

# ConstraintCommons.Automaton — Type. 
julia
Automaton{S, T, F <: Union{S, Vector{S}, Set{S}}} <: AbstractAutomaton
A minimal implementation of a deterministic automaton structure.
source

# ConstraintCommons.accept — Function. 
julia
accept(a::Union{Automaton, MDD}, w)
Return true if a accepts the word w and false otherwise.
source
julia
ConstraintCommons.accept(fa::FakeAutomaton, word)
Implement the accept methods for FakeAutomaton.
source

# ConstraintCommons.at_end — Function. 
julia
at_end(a::Automaton, s)
Internal method used by accept with Automaton.
source

Performances ​
Bench Evolution Automata
Chair Evolution Automata
Bench Evolution Diagrams
Chair Evolution Diagrams
Extensions ​
We extend some operations for Nothing and Symbol.
# ConstraintCommons.symcon — Function. 
julia
symcon(s1::Symbol, s2::Symbol, connector::AbstractString="_")
Extends * to Symbols multiplication by connecting the symbols by an _.
source

# ConstraintCommons.consin — Function. 
julia
consin(::Any, ::Nothing)
Extends Base.in (or ∈) when the set is nothing. Returns false.
source

# ConstraintCommons.consisempty — Function. 
julia
consisempty(::Nothing)
Extends Base.isempty when the set is nothing. Returns true.
source

Performances ​
Bench Evolution Nothing
Chair Evolution Nothing
Bench Evolution Symbols
Chair Evolution Symbols
Sampling ​
During our constraint learning processes, we use sampling to efficiently make partial exploration of search spaces. The following are some examples of sampling utilities.
# ConstraintCommons.oversample — Function. 
julia
oversample(X, f)
Oversample elements of X until the boolean function f has as many true and false configurations.
source

Performances ​
Bench EvolutionChair Evolution
Extrema ​
We need to compute the difference between extrema of various kind of collections in several situations.
# ConstraintCommons.δ_extrema — Function. 
julia
δ_extrema(X...)
Compute both the difference between the maximum and the minimum of over all the collections of X.
source

Performances ​
Bench EvolutionChair Evolution
Dictionaries ​
We provide the ever-useful incsert! function for dictionaries.
# ConstraintCommons.incsert! — Function. 
julia
incsert!(d::Union{AbstractDict, AbstractDictionary}, ind, val = 1)
Increase or insert a counter in a dictionary-based collection. The counter insertion defaults to val = 1.
source

Performances ​
Bench EvolutionChair Evolution

-    
+    
     
   
 
\ No newline at end of file
diff --git a/previews/PR53/constraints/constraint_domains.html b/previews/PR53/constraints/constraint_domains.html
index dadb240..c5d0807 100644
--- a/previews/PR53/constraints/constraint_domains.html
+++ b/previews/PR53/constraints/constraint_domains.html
@@ -8,10 +8,10 @@
     
     
     
-    
+    
     
     
-    
+    
     
     
     
@@ -51,7 +51,7 @@
                 search = :flexible, 
                 solutions_limit = floor(Int, sqrt(max_samplings)))
Create an ExploreSettings object to configure the exploration of a search space composed of a collection of domains.
Arguments
  • domains: A collection of domains to be explored.
  • complete_search_limit: An integer specifying the maximum limit for complete search iterations. Default is 10^6.
  • max_samplings: An integer specifying the maximum number of samplings. Default is the sum of domain sizes.
  • search: A symbol indicating the type of search to perform. Default is :flexible.
  • solutions_limit: An integer specifying the limit on the number of solutions. Default is the floor of the square root of max_samplings.
Returns
  • ExploreSettings object with the specified settings.
source

# ConstraintDomains._explore — Function. 
julia
_explore(args...)
Internals of the explore function. Behavior is automatically adjusted on the kind of exploration: :flexible, :complete, :partial.
source

# ConstraintDomains.explore — Function. 
julia
explore(domains, concept; settings = ExploreSettings(domains), parameters...)
Search (a part of) a search space and return a pair of vectors of configurations: (solutions, non_solutions). The exploration behavior is determined based on the settings.
Arguments
  • domains: A collection of domains to be explored.
  • concept: The concept representing the constraint to be targeted.
  • settings: An optional ExploreSettings object to configure the exploration. Default is ExploreSettings(domains).
  • parameters...: Additional parameters for the concept.
Returns
  • A tuple of sets: (solutions, non_solutions).
source

Performances ​
Bench Evolution ExplorationChair Evolution Exploration
Parameters ​
# ConstraintDomains.BoolParameterDomain — Type. 
julia
BoolParameterDomain <: AbstractDomain
A domain to store boolean values. It is used to generate random parameters.
source

# ConstraintDomains.DimParameterDomain — Type. 
julia
DimParameterDomain <: AbstractDomain
A domain to store dimensions. It is used to generate random parameters.
source

# ConstraintDomains.IdParameterDomain — Type. 
julia
IdParameterDomain <: AbstractDomain
A domain to store ids. It is used to generate random parameters.
source

# ConstraintDomains.FakeAutomaton — Type. 
julia
FakeAutomaton{T} <: ConstraintCommons.AbstractAutomaton
A structure to generate pseudo automaton enough for parameter exploration.
source

# ConstraintCommons.accept — Function. 
julia
accept(a::Union{Automaton, MDD}, w)
Return true if a accepts the word w and false otherwise.
source
julia
ConstraintCommons.accept(fa::FakeAutomaton, word)
Implement the accept methods for FakeAutomaton.
source

# ConstraintDomains.fake_automaton — Function. 
julia
fake_automaton(d)
Construct a FakeAutomaton.
source

# ConstraintDomains.LanguageParameterDomain — Type. 
julia
LanguageParameterDomain <: AbstractDomain
A domain to store languages. It is used to generate random parameters.
source

# ConstraintDomains.OpParameterDomain — Type. 
julia
OpParameterDomain{T} <: AbstractDomain
A domain to store operators. It is used to generate random parameters.
source

# ConstraintDomains.PairVarsParameterDomain — Type. 
julia
PairVarsParameterDomain{T} <: AbstractDomain
A domain to store values paired with variables. It is used to generate random parameters.
source

# ConstraintDomains.ValParameterDomain — Type. 
julia
ValParameterDomain{T} <: AbstractDomain
A domain to store one value. It is used to generate random parameters.
source

# ConstraintDomains.ValsParameterDomain — Type. 
julia
ValsParameterDomain{T} <: AbstractDomain
A domain to store values. It is used to generate random parameters.
source

# Base.rand — Function. 
julia
Base.rand(d::Union{Vector{D},Set{D}, D}) where {D<:AbstractDomain}
Extends Base.rand to (a collection of) domains.
source
julia
Base.rand(itv::Intervals)
 Base.rand(itv::Intervals, i)
Return a random value from itv, specifically from the ith interval if i is specified.
source
julia
Base.rand(d::D) where D <: DiscreteDomain
Draw randomly a point in d.
source
julia
Base.rand(fa::FakeAutomaton)
Extends Base.rand. Currently simply returns fa.
source

# ConstraintDomains.generate_parameters — Function. 
julia
generate_parameters(d<:AbstractDomain, param)
Generates random parameters based on the domain d and the kind of parameters param.
source


-    
+    
     
   
 
\ No newline at end of file
diff --git a/previews/PR53/constraints/constraint_models.html b/previews/PR53/constraints/constraint_models.html
index 1086abd..c0a2454 100644
--- a/previews/PR53/constraints/constraint_models.html
+++ b/previews/PR53/constraints/constraint_models.html
@@ -8,10 +8,10 @@
     
     
     
-    
+    
     
     
-    
+    
     
     
     
@@ -47,7 +47,7 @@
 # Retrieve and display the values
 solution = value.(grid)
 display(solution, Val(:sudoku))
source

- + \ No newline at end of file diff --git a/previews/PR53/constraints/constraints.html b/previews/PR53/constraints/constraints.html index 9c90148..c4ce5b6 100644 --- a/previews/PR53/constraints/constraints.html +++ b/previews/PR53/constraints/constraints.html @@ -8,10 +8,10 @@ - + - + @@ -27,7 +27,7 @@ :val, # one scalar value :vals, # a list of scalar values (independent of the input vector size) ]

source


We provide a couple of methods to navigate the usual constraints extracted from XCSP3-Core.

# Constraints.USUAL_CONSTRAINTS — Constant.
julia
USUAL_CONSTRAINTS::Dict

Dictionary that contains all the usual constraints defined in Constraint.jl. It is based on XCSP3-core specifications available at https://arxiv.org/abs/2009.00514

Adding a new constraint is as simple as defining a new function with the same name as the constraint and using the @usual macro to define it. The macro will take care of adding the new constraint to the USUAL_CONSTRAINTS dictionary.

Example

julia
@usual concept_all_different(x; vals=nothing) = xcsp_all_different(list=x, except=vals)

source


# Constraints.describe — Function.
julia
describe(constraints::Dict{Symbol,Constraint}=USUAL_CONSTRAINTS; width=150)

Return a pretty table with the description of the constraints in constraints.

Arguments

  • constraints::Dict{Symbol,Constraint}: dictionary of constraints to describe. Default is USUAL_CONSTRAINTS.

  • width::Int: width of the table.

Example

julia
describe()

source


# Constraints.constraints_parameters — Function.
julia
constraints_parameters(C=USUAL_CONSTRAINTS)

Return a pretty table with the parameters of the constraints in C.

Arguments

  • C::Dict{Symbol,Constraint}: dictionary of constraints. Default is USUAL_CONSTRAINTS.

Example

julia
constraints_parameters()

source


# Constraints.constraints_descriptions — Function.
julia
constraints_descriptions(C=USUAL_CONSTRAINTS)

Return a pretty table with the descriptions of the constraints in C.

Arguments

  • C::Dict{Symbol,Constraint}: dictionary of constraints. Default is USUAL_CONSTRAINTS.

Example

julia
constraints_descriptions()

source


Concepts, Error Functions, and QUBO matrices ​

One major use of this collection of usual constraint is to extract the concept or the error function (error_f) of a given constraint.

# Constraints.concept — Function.
julia
concept(c::Constraint)

Return the concept (function) of constraint c. concept(c::Constraint, x...; param = nothing) Apply the concept of c to values x and optionally param.

source

julia
concept(s::Symbol, args...; kargs...)

Return the concept of the constraint s applied to args and kargs. This is a shortcut for concept(USUAL_CONSTRAINTS[s])(args...; kargs...).

Arguments

  • s::Symbol: the constraint name.

  • args...: the arguments to apply the concept to.

  • kargs...: the keyword arguments to apply the concept to.

Example

julia
concept(:all_different, [1, 2, 3])

source


Note that the error function is a finer estimation of how much a constraint is violated or not. By default, the error_f method simply return 0. if the constraint is satisfied or 1. otherwise.

Efficient versions of error_f are either hand-coded or generated through CompositionalNetworks.jl.

# Constraints.error_f — Function.
julia
error_f(c::Constraint)

Return the error function of constraint c. error_f(c::Constraint, x; param = nothing) Apply the error function of c to values x and optionally param.

source


Finally, another use is to provide QUBO matrices of those usual constraints through QUBOConstraints.jl. The syntax and interface for this feature are still a work in progress.

- + \ No newline at end of file diff --git a/previews/PR53/constraints/constraints_jl.html b/previews/PR53/constraints/constraints_jl.html index 0eac939..15722f0 100644 --- a/previews/PR53/constraints/constraints_jl.html +++ b/previews/PR53/constraints/constraints_jl.html @@ -8,10 +8,10 @@ - + - + @@ -27,7 +27,7 @@ :val, # one scalar value :vals, # a list of scalar values (independent of the input vector size) ]

source


Concepts, Error Functions, and QUBO matrices ​

One major use of this collection of usual constraint is to extract the concept or the error function (error_f) of a given constraint.

# Constraints.concept — Function.
julia
concept(c::Constraint)

Return the concept (function) of constraint c. concept(c::Constraint, x...; param = nothing) Apply the concept of c to values x and optionally param.

source

julia
concept(s::Symbol, args...; kargs...)

Return the concept of the constraint s applied to args and kargs. This is a shortcut for concept(USUAL_CONSTRAINTS[s])(args...; kargs...).

Arguments

  • s::Symbol: the constraint name.

  • args...: the arguments to apply the concept to.

  • kargs...: the keyword arguments to apply the concept to.

Example

julia
concept(:all_different, [1, 2, 3])

source


Note that the error function is a finer estimation of how much a constraint is violated or not. By default, the error_f method simply return 0. if the constraint is satisfied or 1. otherwise.

Efficient versions of error_r are either hand-coded or generated through CompositionalNetworks.jl.

# Constraints.error_f — Function.
julia
error_f(c::Constraint)

Return the error function of constraint c. error_f(c::Constraint, x; param = nothing) Apply the error function of c to values x and optionally param.

source


Finally, another use is to provide QUBO matrices of those usual constraints through QUBOConstraints.jl. The syntax and interface for this feature are still a work in progress.

Usual Constraints ​

We provide a couple of methods to navigate the usual constraints extracted from XCSP3-Core.

# Constraints.USUAL_CONSTRAINTS — Constant.
julia
USUAL_CONSTRAINTS::Dict

Dictionary that contains all the usual constraints defined in Constraint.jl. It is based on XCSP3-core specifications available at https://arxiv.org/abs/2009.00514

Adding a new constraint is as simple as defining a new function with the same name as the constraint and using the @usual macro to define it. The macro will take care of adding the new constraint to the USUAL_CONSTRAINTS dictionary.

Example

julia
@usual concept_all_different(x; vals=nothing) = xcsp_all_different(list=x, except=vals)

source


# Constraints.describe — Function.
julia
describe(constraints::Dict{Symbol,Constraint}=USUAL_CONSTRAINTS; width=150)

Return a pretty table with the description of the constraints in constraints.

Arguments

  • constraints::Dict{Symbol,Constraint}: dictionary of constraints to describe. Default is USUAL_CONSTRAINTS.

  • width::Int: width of the table.

Example

julia
describe()

source


# Constraints.constraints_parameters — Function.
julia
constraints_parameters(C=USUAL_CONSTRAINTS)

Return a pretty table with the parameters of the constraints in C.

Arguments

  • C::Dict{Symbol,Constraint}: dictionary of constraints. Default is USUAL_CONSTRAINTS.

Example

julia
constraints_parameters()

source


# Constraints.constraints_descriptions — Function.
julia
constraints_descriptions(C=USUAL_CONSTRAINTS)

Return a pretty table with the descriptions of the constraints in C.

Arguments

  • C::Dict{Symbol,Constraint}: dictionary of constraints. Default is USUAL_CONSTRAINTS.

Example

julia
constraints_descriptions()

source


- + \ No newline at end of file diff --git a/previews/PR53/constraints/counting_summing_constraints.html b/previews/PR53/constraints/counting_summing_constraints.html index ee0d07e..2d81b27 100644 --- a/previews/PR53/constraints/counting_summing_constraints.html +++ b/previews/PR53/constraints/counting_summing_constraints.html @@ -8,16 +8,16 @@ - + - - + + -
Julia Constraints

Appearance

Constraints.jl: Streamlining Constraint Definition and Integration in Julia ​

julia
using Constraints
+    
Julia Constraints
Appearance
Constraints.jl: Streamlining Constraint Definition and Integration in Julia ​
julia
using Constraints
 
 @info concept(:sum, [1, 2, 3, 4, 5]; op = ==, val=15)
 @info concept(:sum, [1, 2, 3, 4, 5]; op = ==, val=2)
@@ -39,7 +39,7 @@
 @constraint(model, X in Sum(; op = ==, val = 15))
 @constraint(model, Y in Sum(; op = <=, val = 10))
 JuMP.optimize!(model)
-@info "Sum" value.(X) value.(Y)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
+@info "Sum" value.(X) value.(Y)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
 
 concept(:count, [1,1,1,2], vals = [1, 1, 1, 2], op = ==, val = 4) # true
 concept(:count, [1,1,1,2], vals = [1, 1, 1, 2], op = ==, val = 5) # false
@@ -73,7 +73,7 @@
 @constraint(model, X_at_most in AtMost(vals = [1, 2], val = 1))
 @constraint(model, X_exactly in Exactly(vals = [1, 2], val = 2))
 JuMP.optimize!(model)
-@info "Count" value.(X) value.(X_at_least) value.(X_at_most) value.(X_exactly)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
+@info "Count" value.(X) value.(X_at_least) value.(X_at_most) value.(X_exactly)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
 
 @info concept(:nvalues, [1, 2, 3, 4, 5]; op = ==, val = 5)
 @info concept(:nvalues, [1, 2, 3, 4, 5]; op = ==, val = 2)
@@ -97,7 +97,7 @@
 @constraint(model, Y in NValues(; op = ==, val = 2))
 @constraint(model, Z in NValues(; op = <=, val = 5, vals = [1, 2]))
 JuMP.optimize!(model)
-@info "NValues" value.(X) value.(Y) value.(Z)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
+@info "NValues" value.(X) value.(Y) value.(Z)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
 
 # [v1, v2, v3], [v1, a1, a2; v2, b1, b2; v3, c1, c2] means v1 occurs between a1 and a2 times in the first array, similar for v2 and v3.
 
@@ -163,7 +163,7 @@
 
 co = concept(:cardinality_open)
 co([8, 5, 10, 10]; vals=[2 0 1; 5 1 3; 10 2 3])
source


-    
+    
     
   
 
\ No newline at end of file
diff --git a/previews/PR53/constraints/elementary_constraints.html b/previews/PR53/constraints/elementary_constraints.html
index 58af7be..6f86e35 100644
--- a/previews/PR53/constraints/elementary_constraints.html
+++ b/previews/PR53/constraints/elementary_constraints.html
@@ -8,10 +8,10 @@
     
     
     
-    
+    
     
     
-    
+    
     
     
     
@@ -22,7 +22,7 @@
 
 c([1, 2, 3, 4, 5]; pair_vars=[1, 2, 3, 4, 5])
 c([1, 2, 3, 4, 5]; pair_vars=[1, 2, 3, 4, 6])

source


- + \ No newline at end of file diff --git a/previews/PR53/constraints/generic_constraints.html b/previews/PR53/constraints/generic_constraints.html index 88553a4..4ef5b63 100644 --- a/previews/PR53/constraints/generic_constraints.html +++ b/previews/PR53/constraints/generic_constraints.html @@ -8,11 +8,11 @@ - + - - + + @@ -25,16 +25,16 @@ predicate_dist_different(x) = abs(x[1] - x[2]) ≠ abs(x[3] - x[4]) # Add it to usual constraints -@usual concept_dist_different(x) = xcsp_intension( +@usual concept_dist_different(x) = xcsp_intention( list = x, predicate = predicate_dist_different -)

Please check the section dedicated to the Golomb Ruler problem to see a use for this constraint. <!– TODO: Golomb Ruler –>

APIs ​

Note that the intension constraint is not directly available through the JC-API in Constraints.jl. It is designed as such since defining a constraint through a predicate is the natural way.

We provide here a usage example for the :dist_different constraint, previously added to the USUAL_CONSTRAINTS collection.

Higher level modeling language such as JuMP should provide an Intension interface.

julia
using Constraints
+)

Please check the section dedicated to the Golomb Ruler problem to see a use for this constraint. <!– TODO: Golomb Ruler –>

APIs ​

Note that the intention constraint is not directly available through the JC-API in Constraints.jl. It is designed as such since defining a constraint through a predicate is the natural way.

We provide here a usage example for the :dist_different constraint, previously added to the USUAL_CONSTRAINTS collection.

Higher level modeling language such as JuMP should provide an Intention interface.

julia
using Constraints
 
 concept(:dist_different, x)
 concept(:dist_different)(x)
julia
# Defines the DistDifferent constraint
 using Constraints
 
-c = x -> xcsp_intension(
+c = x -> xcsp_intention(
     list = x,
     predicate = y -> abs(y[1] - y[2]) ≠ abs(y[3] - y[4])
 )
@@ -43,31 +43,71 @@
 c([1, 2, 3, 4]) # false
julia
using CBLS, JuMP
 
 model = Model(CBLS.Optimizer)
+
+# Using build-in DistDifferent
 @variable(model, 0 <= X[1:4] <= 10, Int)
 @constraint(model, X in DistDifferent())
+
+# Alternatively
+@variable(model, 0 <= Y[1:4] <= 10, Int)
+@constraint(model, Y in Intention(y -> abs(y[1] - y[2]) ≠ abs(y[3] - y[4])))
+
 optimize!(model)
 
 @info value.(X)
+@info value.(Y)
julia
using CBLS
+import MathOptInterface as MOI
+
+optimizer = CBLS.Optimizer()
+
+x = MOI.add_variables(optimizer, 4)
+for xi in x
+    # Missing RangeDomain currently in CBLS
+    MOI.add_constraint(optimizer, xi, CBLS.DiscreteSet(collect[1:10]))
+end
+MOI.add_constraint(optimizer, x, CBLS.Intention(y -> abs(y[1] - y[2]) ≠ abs(y[3] - y[4])))
+
+MOI.optimize!(optimizer)

Extension Constraints ​

These are constraints that are defined by explicitly listing all the tuples of values that satisfy the constraint. They are called extensional because they are defined by the set of values they allow. For example, a binary constraint that specifies that a variable X must be either 1 or 2 and a variable Y must be either 3 or 4 could be defined extensionally by the set of tuples (1,3),(1,4),(2,3),(2,4).

These two types of constraints provide a flexible way to define complex relationships between variables in constraint programming.

julia
using Constraints
+
+concept(:dist_different, x)
+concept(:dist_different)(x)
julia
# Defines the DistDifferent constraint
+using Constraints
+
+c = x -> xcsp_intention(
+    list = x,
+    predicate = y -> abs(y[1] - y[2]) ≠ abs(y[3] - y[4])
+)
+
+c([1, 2, 3, 3]) # true
+c([1, 2, 3, 4]) # false
julia
using CBLS, JuMP
+
+model = Model(CBLS.Optimizer)
+
+# Using build-in DistDifferent
+@variable(model, 0 <= X[1:4] <= 10, Int)
+@constraint(model, X in DistDifferent())
+
+# Alternatively
+@variable(model, 0 <= Y[1:4] <= 10, Int)
+@constraint(model, Y in Intention(y -> abs(y[1] - y[2]) ≠ abs(y[3] - y[4])))
+
+optimize!(model)
+
+@info value.(X)
+@info value.(Y)
julia
using CBLS
+import MathOptInterface as MOI
+
+optimizer = CBLS.Optimizer()
+
+x = MOI.add_variables(optimizer, 4)
+for xi in x
+    # Missing RangeDomain currently in CBLS
+    MOI.add_constraint(optimizer, xi, CBLS.DiscreteSet(collect[1:10]))
+end
+MOI.add_constraint(optimizer, x, CBLS.Intention(y -> abs(y[1] - y[2]) ≠ abs(y[3] - y[4])))
 
-# Note that this example gives a solution for the constraint within the interval 0:10
julia
# TODO: How to handle intention in JuMP/MOI

Specific documentation ​

# Constraints.xcsp_intension — Function.
julia
xcsp_intension(list, predicate)

An intensional constraint is usually defined from a predicate over list. As such it encompass any generic constraint.

Arguments

  • list::Vector{Int}: A list of variables

  • predicate::Function: A predicate over list

Variants

  • :dist_different: A constraint ensuring that the distances between marks on the ruler are unique. Specifically, it checks that the distance between x[1] and x[2], and the distance between x[3] and x[4], are different. This constraint is fundamental in ensuring the validity of a Golomb ruler, where no two pairs of marks should have the same distance between them.
julia
concept(:dist_different, x)
-concept(:dist_different)(x)

Examples

@example
2 + 2
@example
2 + 2
@example
using Constraints # hide
-c = concept(:dist_different)
-c([1, 2, 3, 3]) && !c([1, 2, 3, 4])
@example
using Constraints # hide
-c = concept(:dist_different)
-c([1, 2, 3, 3]) && !c([1, 2, 3, 4])

source


Extension Constraints ​

These are constraints that are defined by explicitly listing all the tuples of values that satisfy the constraint. They are called extensional because they are defined by the set of values they allow. For example, a binary constraint that specifies that a variable X must be either 1 or 2 and a variable Y must be either 3 or 4 could be defined extensionally by the set of tuples {(1,3), (1,4), (2,3), (2,4)}.

These two types of constraints provide a flexible way to define complex relationships between variables in constraint programming.

XCSP in Constraints.jl {#XCSP-in-Constraints.jl} ​

# Constraints.xcsp_extension — Function.
julia
xcsp_extension(; list, supports=nothing, conflicts=nothing)

Global constraint enforcing that the tuple x matches a configuration within the supports set pair_vars[1] or does not match any configuration within the conflicts set pair_vars[2]. It embodies the logic: x ∈ pair_vars[1] || x ∉ pair_vars[2], providing a comprehensive way to define valid (supported) and invalid (conflicted) tuples for constraint satisfaction problems. This constraint is versatile, allowing for the explicit delineation of both acceptable and unacceptable configurations.

Arguments

  • list::Vector{Int}: A list of variables

  • supports::Vector{Vector{Int}}: A set of supported tuples. Default to nothing.

  • conflicts::Vector{Vector{Int}}: A set of conflicted tuples. Default to nothing.

Variants

  • :extension: Global constraint enforcing that the tuple x matches a configuration within the supports set pair_vars[1] or does not match any configuration within the conflicts set pair_vars[2]. It embodies the logic: x ∈ pair_vars[1] || x ∉ pair_vars[2], providing a comprehensive way to define valid (supported) and invalid (conflicted) tuples for constraint satisfaction problems. This constraint is versatile, allowing for the explicit delineation of both acceptable and unacceptable configurations.
julia
concept(:extension, x; pair_vars)
-concept(:extension)(x; pair_vars)
  • :supports: Global constraint ensuring that the tuple x matches a configuration listed within the support set pair_vars. This constraint is derived from the extension model, specifying that x must be one of the explicitly defined supported configurations: x ∈ pair_vars. It is utilized to directly declare the tuples that are valid and should be included in the solution space.
julia
concept(:supports, x; pair_vars)
-concept(:supports)(x; pair_vars)
  • :conflicts: Global constraint ensuring that the tuple x does not match any configuration listed within the conflict set pair_vars. This constraint, originating from the extension model, stipulates that x must avoid all configurations defined as conflicts: x ∉ pair_vars. It is useful for specifying tuples that are explicitly forbidden and should be excluded from the solution space.
julia
concept(:conflicts, x; pair_vars)
-concept(:conflicts)(x; pair_vars)

Examples

julia
c = concept(:extension)
-c([1, 2, 3, 4, 5]; pair_vars=[[1, 2, 3, 4, 5]])
-c([1, 2, 3, 4, 5]; pair_vars=([[1, 2, 3, 4, 5]], [[1, 2, 1, 4, 5], [1, 2, 3, 5, 5]]))
-c([1, 2, 3, 4, 5]; pair_vars=[[1, 2, 1, 4, 5], [1, 2, 3, 5, 5]])
-
-c = concept(:supports)
-c([1, 2, 3, 4, 5]; pair_vars=[[1, 2, 3, 4, 5]])
-
-c = concept(:conflicts)
-c([1, 2, 3, 4, 5]; pair_vars=[[1, 2, 1, 4, 5], [1, 2, 3, 5, 5]])

source


- +MOI.optimize!(optimizer) + \ No newline at end of file diff --git a/previews/PR53/constraints/graph_constraints.html b/previews/PR53/constraints/graph_constraints.html index 41d4d8e..9ef048e 100644 --- a/previews/PR53/constraints/graph_constraints.html +++ b/previews/PR53/constraints/graph_constraints.html @@ -8,10 +8,10 @@ - + - + @@ -24,7 +24,7 @@ c([2, 3, 4, 1]) c([2, 3, 1, 4]; op = ==, val = 3) c([4, 3, 1, 3]; op = >, val = 0)

source


- + \ No newline at end of file diff --git a/previews/PR53/constraints/intro.html b/previews/PR53/constraints/intro.html index 7d12d80..0a2f197 100644 --- a/previews/PR53/constraints/intro.html +++ b/previews/PR53/constraints/intro.html @@ -8,17 +8,17 @@ - + - +
Julia Constraints

Appearance

Introduction to basics constraint-based modeling tools ​

Constraint programming (CP) is a high-level paradigm for solving combinatorial problems, and Julia Constraints provides an efficient and flexible framework for developing constraint-based models.

All along this documentation, we will present code example base on the syntaxes of Julia Constraints internals (JC-API), of Julia for Mathematical Programming (JuMP ), of MathOptInterface (MOI), and, when relevant, of other standards such as XCSP.

Terminology ​

Warning

Terminology in Optimization varies strongly between different methods and communities. In this doc we try to be consistent with the following principles (in bold).

  • Constraint: A general mathematical predicate involving variables.

  • Constraint Instantiation: The application of a constraint to specific variables.

  • Configuration: A specific assignment of values to the variables.

  • Constraint Satisfaction/Violation: Whether a configuration meets or fails a constraint.

Constraint ​

Definition: A constraint is a formal mathematical statement that expresses a condition or a relation between a set of variables. It can be seen as a predicate that the variables must satisfy.

Example: Consider the constraint x+y≤10. This constraint involves two variables, x and y, and specifies that their sum must not exceed 10.

Constraint Instantiation ​

Definition: A constraint instantiation refers to a specific application of a generic constraint to a particular subset of variables from a problem. It is essentially the constraint applied with the actual variables of the problem.

Example: Given the generic constraint x+y≤10, if we have variables x1 and x2 in our problem, then the instantiated constraint would be x1+x2≤10.

Configuration ​

Definition: A configuration, also known as an assignment, is a specific set of values assigned to the variables in their respective domains. It represents a possible state of the variables.

Example: For variables x and y with domains [0,10], a configuration could be x=3 and y=2.

Constraint Satisfaction or Violation by a Configuration ​

Definition: This refers to whether a specific configuration (set of variable assignments) satisfies or violates a given constraint instantiation. A constraint is satisfied if the configuration makes the constraint true; otherwise, it is violated (false).

Example: Given the constraint instantiation x+y≤10 and the configuration x=3 and y=2, the constraint is satisfied because 3+2=5, which is less than or equal to 10. However, for the configuration x=6 and y=5, the constraint is violated because 6+5=11, which exceeds 10.

Domain-defined variables ​

In CP, variables are defined through their domain. ConstraintDomains.jl supports various types of domains such as discrete ones (sets, range, etc.), or continuous intervals, and custom domains.

A versatile constraints' API ​

Constraints.jl implements a wide range of generic and core constraints, ensuring compatibility with XCSP3-core standards and providing a user-friendly interface. It includes features extracted from the learning blocks of Julia Constraints to leverage most of each constraint characteristics.

A collection of models ​

The ConstraintModels.jl catalog offers a collection of predefined models and templates for constructing complex constraint satisfaction problems (CSPs) and optimization models. This resource provides reusable components to streamline the modeling process.

Contributions with new models are more than welcome!

Internal Aspects ​

Several internal components are crucial for the efficient functioning of Julia Constraints. ConstraintCommons.jl provides shared functionalities and utilities used across different parts of the framework, contributing to its robust performance and extensibility. However, it is unlikely to be of direct use to most users.

- + \ No newline at end of file diff --git a/previews/PR53/constraints/language_constraints.html b/previews/PR53/constraints/language_constraints.html index bff78fc..fd12d3d 100644 --- a/previews/PR53/constraints/language_constraints.html +++ b/previews/PR53/constraints/language_constraints.html @@ -8,10 +8,10 @@ - + - + @@ -65,7 +65,7 @@ c([2,1,2]; language = a) c([1,0,2]; language = a) c([0,1,2]; language = a)

source


- + \ No newline at end of file diff --git a/previews/PR53/constraints/packing_scheduling_constraints.html b/previews/PR53/constraints/packing_scheduling_constraints.html index 314c9c6..75c8392 100644 --- a/previews/PR53/constraints/packing_scheduling_constraints.html +++ b/previews/PR53/constraints/packing_scheduling_constraints.html @@ -8,16 +8,16 @@ - + - - + + -
Julia Constraints

Appearance

Constraints.jl: Streamlining Constraint Definition and Integration in Julia ​

julia
using Constraints
+    
Julia Constraints
Appearance
Constraints.jl: Streamlining Constraint Definition and Integration in Julia ​
julia
using Constraints
 
 @info concept(:cumulative, [1, 2, 3, 4, 5]; val = 1)
 @info concept(:cumulative, [1, 2, 2, 4, 5]; val = 1)
@@ -44,7 +44,7 @@
 @constraint(model,
     Z in Cumulative(; pair_vars = [3 2 5 4 2; 1 2 1 1 3], op = <, val = 5))
 JuMP.optimize!(model)
-@info "Cumulative" value.(X) value.(Y) value.(Z)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
+@info "Cumulative" value.(X) value.(Y) value.(Z)
julia
# TODO: How to handle intention in JuMP/MOI
julia
using Constraints
 
 @info concept(:no_overlap, [1, 2, 3, 4, 5])
 @info concept(:no_overlap, [1, 2, 3, 4, 1])
@@ -88,7 +88,7 @@
 c([1, 2, 4, 6, 3]; pair_vars = [1, 1, 3, 1, 1])
 c([1, 1, 1, 3, 5, 2, 7, 7, 5, 12, 8, 7]; pair_vars = [2, 4, 1, 4 ,2 ,3, 5, 1, 2, 3, 3, 2], dim = 3)
 c([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4]; pair_vars = [2, 4, 1, 4 ,2 ,3, 5, 1, 2, 3, 3, 2], dim = 3)
source


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diff --git a/previews/PR53/constraints/variables_and_domains.html b/previews/PR53/constraints/variables_and_domains.html
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Julia Constraints
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Defining Variables and Exploring Domains ​
ConstraintDomains.jl stands as the standard way to define variables and explore domains within the Julia Constraints ecosystem. This package provides the infrastructure necessary for specifying both discrete and continuous domains. Explorations features are mainly related to the learning about constraints aspect and will be detailed in that chapter.
Variables and their domains can also be defined through MOI and JuMP syntaxes in their respective models.
Implementing the AbstractDomain Interface ​
At the foundation of ConstraintDomains.jl is the AbstractDomain type, an abstract supertype for all domain types. Implementations of AbstractDomain must provide methods for checking membership (∈), generating random elements (rand), and determining the domain's size or range (length). These functionalities are essential for defining the behavior and properties of variable domains within constraint models.
# ConstraintDomains.AbstractDomain — Type. 
julia
AbstractDomain
An abstract super type for any domain type. A domain type D <: AbstractDomain must implement the following methods to properly interface AbstractDomain.
  • Base.∈(val, ::D)
  • Base.rand(::D)
  • Base.length(::D) that is the number of elements in a discrete domain, and the distance between bounds or similar for a continuous domain
Additionally, if the domain is used in a dynamic context, it can extend
  • add!(::D, args)
  • delete!(::D, args)
where args depends on D's structure
source

Discrete Domains ​
Optimization in discrete spaces has been the core of Constraint Programming since its inception. We provide three kinds of discrete domains.
SetDomain ​
A SetDomain is simply a Set of unordered numerical values.
julia
using ConstraintDomains
+    
Julia Constraints
Appearance
Defining Variables and Exploring Domains ​
ConstraintDomains.jl stands as the standard way to define variables and explore domains within the Julia Constraints ecosystem. This package provides the infrastructure necessary for specifying both discrete and continuous domains. Explorations features are mainly related to the learning about constraints aspect and will be detailed in that chapter.
Variables and their domains can also be defined through MOI and JuMP syntaxes in their respective models.
Implementing the AbstractDomain Interface ​
At the foundation of ConstraintDomains.jl is the AbstractDomain type, an abstract supertype for all domain types. Implementations of AbstractDomain must provide methods for checking membership (∈), generating random elements (rand), and determining the domain's size or range (length). These functionalities are essential for defining the behavior and properties of variable domains within constraint models.
# ConstraintDomains.AbstractDomain — Type. 
julia
AbstractDomain
An abstract super type for any domain type. A domain type D <: AbstractDomain must implement the following methods to properly interface AbstractDomain.
  • Base.∈(val, ::D)
  • Base.rand(::D)
  • Base.length(::D) that is the number of elements in a discrete domain, and the distance between bounds or similar for a continuous domain
Additionally, if the domain is used in a dynamic context, it can extend
  • add!(::D, args)
  • delete!(::D, args)
where args depends on D's structure
source

Discrete Domains ​
Optimization in discrete spaces has been the core of Constraint Programming since its inception. We provide three kinds of discrete domains.
SetDomain ​
A SetDomain is simply a Set of unordered numerical values.
julia
using ConstraintDomains
 
 d1 = domain([53.69, 89.2, 0.12])
 d2 = domain([2//3, 89//123])
@@ -44,7 +44,7 @@
 MOI.add_constraint(optimizer, v3, CBLS.DiscreteSet(4.3))
 
 v4 = MOI.add_variable(optimizer)
-MOI.add_constraint(optimizer, v4, CBLS.DiscreteSet([1, 42, 3.14]))
RangeDomain ​
A range domain allows for minimal storage and more efficient operation on discrete sets defined as Range in Julia. It is not recommended for dynamic domains (it will be replaced with SetDomain as soon as a non-extremal element is removed).
julia
using ConstraintDomains
+MOI.add_constraint(optimizer, v4, CBLS.DiscreteSet([1, 42, 3.14]))
RangeDomain ​
A range domain allows for minimal storage and more efficient operation on discrete sets defined as Range in Julia. It is not recommended for dynamic domains (it will be replaced with SetDomain as soon as a non-extremal element is removed).
julia
using ConstraintDomains
 
 d1 = domain(1:5)
 d2 = domain(0.4:0.1:1.3)
julia
## To be implemented
@@ -64,12 +64,12 @@
 
 # v2 = MOI.add_variable(optimizer)
 
-# MOI.add_constraint(optimizer, v1, MOI.RangeSet(0.4:0.1:1.3))
Arbitrary Domains ​
As odd as it may sound, we provide a constructor for sets of elements making up arbitrary, possibly non-numerical, domains.
Until some practical examples are implemented, this structure will mainly be a placeholder with default behavior.
Continuous Domains ​
Numerous problems cannot be challenged without expressing at least part of their domains as continuous variables. In Julia Constraints we provide such domains as (set of) intervals.
julia
using ConstraintDomains, Intervals
+# MOI.add_constraint(optimizer, v1, MOI.RangeSet(0.4:0.1:1.3))
Arbitrary Domains ​
As odd as it may sound, we provide a constructor for sets of elements making up arbitrary, possibly non-numerical, domains.
Until some practical examples are implemented, this structure will mainly be a placeholder with default behavior.
Continuous Domains ​
Numerous problems cannot be challenged without expressing at least part of their domains as continuous variables. In Julia Constraints we provide such domains as (set of) intervals.
julia
using ConstraintDomains, Intervals
 
 d1 = domain(Interval{Open,Closed}(3.2, true), (42, false))
 d2 = domain(3.2..42)
 d3 = domain([3.2..42, 63.2..324.1])
julia
## see MOI.Interval
julia
## see MOI.Interval

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Julia Constraints
Appearance
Advanced Constraint Programming Techniques ​
Global Constraints and Their Uses ​
  • Dive deeper into global constraints and how they simplify complex problems.
Search Strategies and Optimization ​
  • Discuss various search strategies and their impact on solving CP problems.

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Julia Constraints
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Applying Optimization Methods ​
Case Studies and Real-World Applications ​
  • Showcase studies where CP and optimization have been successfully applied.
From Theory to Practice ​
  • Guide readers through the process of formulating and solving an optimization problem from a real-world scenario.

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index ee6f6f2..097322c 100644
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Julia Constraints
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Community and Contribution ​
Joining the JuliaConstraint Community ​
  • Encourage readers to join the community, highlighting how they can contribute and collaborate.
Future Directions ​
  • Share the vision for JuliaConstraint and upcoming projects or areas of research.

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Julia Constraints
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Constraint Programming 101 ​
What is Constraint Programming? ​
  • Define CP and its significance in solving combinatorial problems.
Basic Concepts and Terminology ​
  • Introduce key concepts such as constraints, domains, and variables.
How CP differs from other optimization techniques ​
  • Contrast with other methods like linear programming and metaheuristics.

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Julia Constraints
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Exploring JuliaConstraint Packages ​
Package Overviews ​
  • Introduce each package within the JuliaConstraint organization, its purpose, and primary features.
Installation and Getting Started Guides ​
  • Provide step-by-step instructions for installing and getting started with each package.

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Julia Constraints
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Getting Started with Julia for CP and Optimization ​
Why Julia? ​
  • Discuss the advantages of Julia for computational science and optimization, highlighting its performance and ease of use.
Setting Up Your Julia Environment ​
We encourage users to install Julia through juliaup, a version manager for the Julia language. Please look at the official Julia language download page for further information. Once installed, Julia can be used through various editors (Visual Studio Code), notebooks (Pluto.jl), or command-line (REPL).
Although a part of the CP solvers available within the Julia ecosystem have their own interface, we encourage users to use the JuMP modeling language if possible.
Julia Constraints host several solvers(' interfaces). Due to its flexibility in modeling and solving, we will use LocalSearchSolvers.jl through its JuMP interface CBLS.jl as the basic example. Note that depending on the targeted instances, available hardware, and expectations, it is not necessarily the best choice.
All along the documentation, we will try to provide syntax examples for different setup.
julia
using LocalSearchSolvers
julia
using JuMP, CBLS
julia
# TODO: Add other solvers
Your First Julia CP Model ​
We will start with a classic puzzle game and some of its not that simple variants: the Sudoku.
(From Wikipedia) In classic Sudoku, the objective is to fill a 9 × 9 grid with digits so that each column, each row, and each of the nine 3 × 3 subgrids that compose the grid (also called "boxes", "blocks", or "regions") contains all of the digits from 1 to 9. The puzzle setter provides a partially completed grid, which for a well-posed puzzle has a single solution.
Constraint Programming follows the model-and-solve approach. We first need to model our Sudoku problem.
julia
m = JuMP.Model(CBLS.Optimizer)
julia
# TODO: Add other solvers
But what are the basis of CP models? It is quite simple:
  1. A collection X=X1,⋯,Xn of variables with each an associated domain.
julia
@variable(m, 1 ≤ X[1:9, 1:9] ≤ 9, Int)
julia
# TODO: Add other solvers
  1. A collection of predicates (called constraints) C=C1,⋯,Cn over (subsets of) X.
When modeling problems as CP, one might define and use their own predicates. However, a large collection of already defined constraints exists. One, if not the most, iconic global constraint is called AllDifferent. It ensures that all variables take distinct values.
Sudoku puzzles can be defined using only this one constraint applied to different subsets of variables.
julia
for i in 1:9
+    
Julia Constraints
Appearance
Getting Started with Julia for CP and Optimization ​
Why Julia? ​
  • Discuss the advantages of Julia for computational science and optimization, highlighting its performance and ease of use.
Setting Up Your Julia Environment ​
We encourage users to install Julia through juliaup, a version manager for the Julia language. Please look at the official Julia language download page for further information. Once installed, Julia can be used through various editors (Visual Studio Code), notebooks (Pluto.jl), or command-line (REPL).
Although a part of the CP solvers available within the Julia ecosystem have their own interface, we encourage users to use the JuMP modeling language if possible.
Julia Constraints host several solvers(' interfaces). Due to its flexibility in modeling and solving, we will use LocalSearchSolvers.jl through its JuMP interface CBLS.jl as the basic example. Note that depending on the targeted instances, available hardware, and expectations, it is not necessarily the best choice.
All along the documentation, we will try to provide syntax examples for different setup.
julia
using LocalSearchSolvers
julia
using JuMP, CBLS
julia
# TODO: Add other solvers
Your First Julia CP Model ​
We will start with a classic puzzle game and some of its not that simple variants: the Sudoku.
(From Wikipedia) In classic Sudoku, the objective is to fill a 9 × 9 grid with digits so that each column, each row, and each of the nine 3 × 3 subgrids that compose the grid (also called "boxes", "blocks", or "regions") contains all of the digits from 1 to 9. The puzzle setter provides a partially completed grid, which for a well-posed puzzle has a single solution.
Constraint Programming follows the model-and-solve approach. We first need to model our Sudoku problem.
julia
m = JuMP.Model(CBLS.Optimizer)
julia
# TODO: Add other solvers
But what are the basis of CP models? It is quite simple:
  1. A collection X=X1,⋯,Xn of variables with each an associated domain.
julia
@variable(m, 1 ≤ X[1:9, 1:9] ≤ 9, Int)
julia
# TODO: Add other solvers
  1. A collection of predicates (called constraints) C=C1,⋯,Cn over (subsets of) X.
When modeling problems as CP, one might define and use their own predicates. However, a large collection of already defined constraints exists. One, if not the most, iconic global constraint is called AllDifferent. It ensures that all variables take distinct values.
Sudoku puzzles can be defined using only this one constraint applied to different subsets of variables.
julia
for i in 1:9
         @constraint(m, X[i,:] in AllDifferent()) # rows
         @constraint(m, X[:,i] in AllDifferent()) # columns
-end
julia
# TODO: Add other solvers
The last series of AllDifferent constraint is less straight forward. We need to ensure that each 3 × 3 subgrid (block) is filled with distinct values.
julia
for i in 0:2, j in 0:2 # blocks
+end
julia
# TODO: Add other solvers
The last series of AllDifferent constraint is less straight forward. We need to ensure that each 3 × 3 subgrid (block) is filled with distinct values.
julia
for i in 0:2, j in 0:2 # blocks
     @constraint(
         m,
         vec(X[(3i+1):(3(i+1)), (3j+1):(3(j+1))]) in AllDifferent(),
     )
-end
julia
# TODO: Add other solvers
We can now simply run our solver to look for a feasible solution.
julia
optimize!(m)
Note that this is heuristic solver, we might not get a feasible solution! Let's check it out. The value function print the value of a JuMP variable. We can cast it over a collection with the value. syntax.
julia
value.(X)

-    
+end
julia
# TODO: Add other solvers
We can now simply run our solver to look for a feasible solution.
julia
optimize!(m)
Note that this is heuristic solver, we might not get a feasible solution! Let's check it out. The value function print the value of a JuMP variable. We can cast it over a collection with the value. syntax.
julia
value.(X)

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Julia Constraints
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Welcome to Julia Constraints ​
An introductory post/chapter that provides an overview of the JuliaConstraint organization, its mission, and what readers can expect to learn from the content. Highlight the importance of Constraint Programming (CP) and optimization in solving real-world problems.

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Julia Constraints
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Building and Analyzing Models ​
Modeling Best Practices ​
  • Share best practices and tips for building efficient CP and optimization models.
Performance Analysis and Improvement ​
  • Teach how to analyze and improve the performance of models.

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Julia Constraints
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Dive into Optimization ​
Understanding Optimization ​
  • Explanation of optimization, types of optimization problems (e.g., linear, nonlinear, integer programming).
Metaheuristics Overview ​
  • Introduce concepts like Genetic Algorithms, Simulated Annealing, and Tabu Search.
Mathematical Programming Basics ​
  • Cover the fundamentals of mathematical programming and its role in optimization.

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Julia Constraints
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Tutorials and Experiments ​
Hands-On Tutorials ​
  • Provide step-by-step tutorials covering various topics and complexity levels.
Experimental Analysis ​
  • Discuss the importance of experimental analysis in CP and how to conduct meaningful experiments.

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 tr_val_minus_var(x, X::AbstractVector; val)
Return the difference val - x[i] if positive, 0.0 otherwise. Extended method to vector with sig (x, val) are generated. When X is provided, the result is computed without allocations.
source

# CompositionalNetworks.tr_var_minus_val — Method. 
julia
tr_var_minus_val(i, x; val)
 tr_var_minus_val(x; val)
 tr_var_minus_val(x, X::AbstractVector; val)
Return the difference x[i] - val if positive, 0.0 otherwise. Extended method to vector with sig (x, val) are generated. When X is provided, the result is computed without allocations.
source

# CompositionalNetworks.transformation_layer — Function. 
julia
transformation_layer(param = Vector{Symbol}())
Generate the layer of transformations functions of the ICN. Iff param value is non empty, also includes all the related parametric transformations.
source

# CompositionalNetworks.weights! — Method. 
julia
weights!(icn, weights)
Set the weights of an ICN with a BitVector.
source

# CompositionalNetworks.weights — Method. 
julia
weights(icn)
Access the current set of weights of an ICN.
source

# CompositionalNetworks.weights_bias — Method. 
julia
weights_bias(x)
A metric that bias x towards operations with a lower bit. Do not affect the main metric.
source

# QUBOConstraints.AbstractOptimizer — Type. 
julia
AbstractOptimizer
An abstract type (interface) used to learn QUBO matrices from constraints. Only a train method is required.
source

# QUBOConstraints.QUBO_base — Function. 
julia
QUBO_base(n, weight = 1)
A basic QUBO matrix to ensure that binarized variables keep a valid encoding.
source

# QUBOConstraints.QUBO_linear_sum — Method. 
julia
QUBO_linear_sum(n, σ)
One valid QUBO matrix given n variables and parameter σ for the linear sum constraint.
source

# QUBOConstraints.binarize — Method. 
julia
binarize(x[, domain]; binarization = :one_hot)
Binarize x following the binarization encoding. If x is a vector (instead of a number per say), domain is optional.
source

# QUBOConstraints.debinarize — Method. 
julia
debinarize(x[, domain]; binarization = :one_hot)
Transform a binary vector into a number or a set of number. If domain is not given, it will compute a default value based on binarization and x.
source

# QUBOConstraints.is_valid — Function. 
julia
is_valid(x, encoding::Symbol = :none)
Check if x has a valid format for encoding.
For instance, if encoding == :one_hot, at most one bit of x can be set to 1.
source

# QUBOConstraints.train — Method. 
julia
train(args...)
Default train method for any AbstractOptimizer.
source


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@@ -1 +1 @@
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+{"constraints_comparison_constraints.md":"BzzoUfWs","constraints_connection_constraints.md":"B5jEZYFf","constraints_constraint_commons.md":"Jf9w5IBz","constraints_constraint_domains.md":"18VSjCU9","constraints_constraint_models.md":"CrunAeM3","constraints_constraints.md":"DWM2vBTB","constraints_constraints_jl.md":"BdLeqbl5","constraints_counting_summing_constraints.md":"BrFsDYjC","constraints_elementary_constraints.md":"DxnyLzjm","constraints_generic_constraints.md":"BMouGtoq","constraints_graph_constraints.md":"CR1DArw6","constraints_intro.md":"mZ_BVjmf","constraints_language_constraints.md":"BUjtQKnc","constraints_packing_scheduling_constraints.md":"6H7cStQm","constraints_variables_and_domains.md":"hAN5u75S","cp_advanced.md":"DbshrWLB","cp_applications.md":"BzEmtekX","cp_contribution.md":"DvP7K9sz","cp_cp101.md":"D4_luP8w","cp_ecosystem.md":"DtLmIZoC","cp_getting_started.md":"CtxbRJ9l","cp_intro.md":"BzAy_7dt","cp_models.md":"DDGW0m0s","cp_opt.md":"CQ5Q5a1L","cp_tuto_xp.md":"B314RgEu","full_api.md":"BkPkLvWN","index-old.md":"DdiyV8hp","index.md":"DDblyEKU","learning_aggregation.md":"CDATAhnH","learning_arithmetic.md":"DmwRP2jg","learning_comparison.md":"DOlPi07g","learning_compositional_networks.md":"DZiVyN4n","learning_constraint_learning.md":"DnMq1k2p","learning_intro.md":"i5256_Vr","learning_layers.md":"DUYHEve0","learning_qubo_constraints.md":"C-jSP5hF","learning_qubo_encoding.md":"Civv8aoX","learning_qubo_learning.md":"dwDHs_At","learning_transformation.md":"BQMJx5Bn","meta_meta_strategist.md":"B_rz2YWD","perf_api.md":"SF8LKTTz","perf_benchmark_ext.md":"uftm8iDY","perf_chairmarks_ext.md":"BS45XV9r","perf_perf_checker.md":"Br3eypFF","perf_perf_interface.md":"C5fMYDZM","perf_tutorial.md":"DhWXid5p","public_api.md":"Dtk4dQBg","solvers_cbls.md":"DUlBy74r","solvers_intro.md":"BRYu-Zxp","solvers_local_search_solvers.md":"C25Onbg2"}
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Julia Constraints
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JuliaConstraints ​
JuliaConstraints is a collection of packages that help you solve constraint programming problems in Julia. Constraint programming involves modeling problems with constraints, such as "x > 5" or "x + y = 10", and finding solutions that satisfy all of the constraints. It is a part of the JuMP ecosystem that focuses on constraint programming in Julia.
The goal of packages in JuliaConstraints are two-fold: some of them provide a generic interface, others are solvers for CP models (either purely in Julia or wrapping). They make it easy to solve constraint-satisfaction problems (CSPs) and constraint-optimisation problems (COPs) in Julia using industry-standard solvers and mixed-integer solvers.
Other packages for CP in Julia include:
Operational Research vs Constraint Programming ​
Operational research (OR) is a problem-solving approach that uses mathematical models, statistical analysis, and optimization techniques to help organizations make better decisions. OR is concerned with understanding and optimizing complex systems, such as supply chains, transportation networks, and manufacturing processes, to improve efficiency and reduce costs.
On the other hand, constraint programming (CP) is a programming paradigm that focuses on solving problems with constraints. Constraints are conditions that must be satisfied for a solution to be valid. CP is often used to solve combinatorial problems, such as scheduling, routing, and allocation, where the search space of possible solutions is very large.
So, while both OR and CP are concerned with solving complex problems, they approach the problem-solving process from different angles. OR typically uses mathematical models and optimization techniques to analyze and optimize existing systems, while CP focuses on finding valid solutions that satisfy a set of constraints.
Constraint-based local search (CBLS) is a type of constraint programming solver that uses a heuristic search algorithm to find solutions to problems. It starts with an initial solution and tries to improve it by making small changes that satisfy the constraints. CBLS is especially useful for large and complex problems where finding an exact solution may take too much time or be impossible.
In contrast, other constraint programming solvers use a variety of algorithms and techniques to find exact solutions to problems. These solvers try to find a solution that satisfies all of the constraints in the problem. They can be useful for smaller problems where finding an exact solution is feasible, or for problems that have a clear mathematical structure.
In summary, CBLS is a type of constraint programming solver that uses a heuristic search algorithm to find good solutions, while other constraint programming solvers use various techniques to find exact solutions to problems.

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Julia Constraints
Appearance
Julia Constraints
Model Smoothly Decide Wisely
A Toolkit for Constraint Programming
Model, Learn, and Solve!
View on Github
JuliaConstraints
JuMP.jl
JuMP.jl
Model optimization problems via JuMP.jl!
PerfChecker.jl
PerfChecker.jl
Cross-version performance checking tool
Pluto.jl
Pluto.jl
Simple, reactive programming environment via Julia notebooks
What is Julia Constraints?
The Julia Constraints organization serves as a hub for resources to create, understand, and solve optimization through the lens of Constraint Programming. Our goal is to make Constraint Programming accessible and efficient for users at all levels of expertise, by providing a comprehensive suite of tools.
Most tools integrate seamlessly with JuMP, a popular Julia package for mathematical optimization.
Ecosystem overview
Core Packages
The foundation of common packages that provide essential features for constraint programming ensures that users possess the fundamental tools required for their projects.
  • ConstraintCommons.jl is designed to make constraint programming solutions in Julia interoperable. It provides shared structures, abstract types, functions, and generic methods used by both basic feature packages and learning-oriented packages.
  • ConstraintDomains.jl focuses on the definition and manipulation of variable domains, which are used to solve constraint programming problems. This package provides the infrastructure needed to specify both discrete and continuous domains, allowing a wide range of constraint programming applications.
  • Constraints.jl is a key component, specifically designed to facilitate the definition, manipulation, and application of constraints in constraint programming. This package is central to handling both standard and complex constraints, making it an indispensable tool for developers and researchers working in CP.
  • ConstraintModels.jl is a package for Julia Constraints' solvers that stores Constraint Programming models.
Learning and Translation Tools
A collection that bridges the gap between the ease of modeling and computational efficacy. These tools learn from constraints or convert natural language problems into constraint programming solutions, requiring minimal input from the user beyond the model itself.
  • CompositionalNetworks.jl provides interpretable compositional networks (ICN), a combinatorial variant of neural networks that allows the user to obtain interpretable results, unlike regular artificial neural networks.
  • QUBOConstraints.jl is a package that can (automatically) learn QUBO matrices from optimization constraints.
  • ConstraintsTranslator.jl (tentative name, WIP) is a tool for converting problems expressed in natural language into optimization models.
  • ConstraintLearning.jl is a common interface that integrates the various components outlined above.
Solvers
We offer a variety of solvers, from native Julia solvers to interfaces with JuMP for external CP solvers, to cater to various problem-solving needs.
  • LocalSearchSolvers.jl is a Julia native framework to (semi-)automatically build Constraint-based Local Search solvers. It serves as a basic for the experimental design or core and learning oriented packages in Julia Constraints.
  • CBLS.jl a MOI/JuMP interface for the above framework!
  • CPLEXCP.jl a Julia interface for CPLEX CP Optimizer.
  • Chuffed.jl a wrapper for the constraint-programming solver Chuffed to Julia.
  • JaCoP.jl a Julia interface for the JaCoP constraint-programming solver.
JuMP extras
Constraint Programming is slowly making steps into the main JuMP components. However, some extra resources are available as
Meta-solving
MetaStrategist.jl is a meta-solving package in its formative stages, which aims to harness the strengths of CP and JuMP. Its goal is to formulate tailored strategies that take into consideration the unique hardware and software resources at hand, offering a new horizon in problem-solving efficiency and adaptability. Stay tuned!
Performance related tools
We've made a tool for cross-version performance checking that ensures the high efficiency and reliability of our solutions. By facilitating clear and simple performance evaluations, PerfChecker.jl enhances both development and maintenance, contributing to the overall health and progress of Julia (Constraints)'s growing library of resources.
Contributors Page
Acknowledgments
The Julia Constraints community would not be where it is today without the collective efforts of many talented individuals and organizations. We extend our heartfelt thanks to:
  • IIJ Research Lab: The driving force behind more than half of this project!
  • JuMP-dev Community: For their extensive contributions to the development of our packages.
  • Individual Contributors: Numerous developers and researchers who have dedicated their time and skills to enhance our tools.

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Julia Constraints
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Aggregation Layer ​
Some text to describe the aggragation layer within usual ICNs.
List of aggregations ​
# CompositionalNetworks.ag_sum — Function. 
julia
ag_sum(x)
Aggregate through + a vector into a single scalar.
source

# CompositionalNetworks.ag_count_positive — Function. 
julia
ag_count_positive(x)
Count the number of strictly positive elements of x.
source

Layer generation ​
# CompositionalNetworks.aggregation_layer — Function. 
julia
aggregation_layer()
Generate the layer of aggregations of the ICN. The operations are mutually exclusive, that is only one will be selected.
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Julia Constraints
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Arithmetic Layer ​
Some text to describe the arithmetic layer within usual ICNs.
List of arithmetic operations ​
# CompositionalNetworks.ar_sum — Function. 
julia
ar_sum(x)
Reduce k = length(x) vectors through sum to a single vector.
source

# CompositionalNetworks.ar_prod — Function. 
julia
ar_prod(x)
Reduce k = length(x) vectors through product to a single vector.
source

Layer generation ​
# CompositionalNetworks.arithmetic_layer — Function. 
julia
arithmetic_layer()
Generate the layer of arithmetic operations of the ICN. The operations are mutually exclusive, that is only one will be selected.
source


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Julia Constraints
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Comparison Layer ​
Some text to describe the comparison layer within usual ICNs.
List of comparisons ​
List the possible parameters and how it affects the comparison.
Non-parametric ​
# CompositionalNetworks.co_identity — Function. 
julia
co_identity(x)
Identity function. Already defined in Julia as identity, specialized for scalars in the comparison layer.
source

Missing docstring.
Missing docstring for co_euclidian. Check Documenter's build log for details.
Missing docstring.
Missing docstring for co_abs_diff_val_vars. Check Documenter's build log for details.
Missing docstring.
Missing docstring for co_val_minus_vars. Check Documenter's build log for details.
Missing docstring.
Missing docstring for co_vars_minus_val. Check Documenter's build log for details.
Param: :val ​
Missing docstring.
Missing docstring for co_abs_diff_val_param. Check Documenter's build log for details.
Missing docstring.
Missing docstring for co_val_minus_param. Check Documenter's build log for details.
Missing docstring.
Missing docstring for co_param_minus_val. Check Documenter's build log for details.
Missing docstring.
Missing docstring for co_euclidian_param. Check Documenter's build log for details.
Layer generation ​
Missing docstring.
Missing docstring for make_comparisons. Check Documenter's build log for details.
# CompositionalNetworks.comparison_layer — Function. 
julia
comparison_layer(param = false)
Generate the layer of transformations functions of the ICN. Iff param value is set, also includes all the parametric comparison with that value. The operations are mutually exclusive, that is only one will be selected.
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Julia Constraints
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CompositionalNetworks.jl ​
Documentation for CompositionalNetworks.jl.
Utilities ​
# CompositionalNetworks.map_tr! — Function. 
julia
map_tr!(f, x, X, param)
Return an anonymous function that applies f to all elements of x and store the result in X, with a parameter param (which is set to nothing for function with no parameter).
source

# CompositionalNetworks.lazy — Function. 
julia
lazy(funcs::Function...)
Generate methods extended to a vector instead of one of its components. A function f should have the following signature: f(i::Int, x::V).
source

# CompositionalNetworks.lazy_param — Function. 
julia
lazy_param(funcs::Function...)
Generate methods extended to a vector instead of one of its components. A function f should have the following signature: f(i::Int, x::V; param).
source

# CompositionalNetworks.as_bitvector — Function. 
julia
as_bitvector(n::Int, max_n::Int = n)
Convert an Int to a BitVector of minimal size (relatively to max_n).
source

# CompositionalNetworks.as_int — Function. 
julia
as_int(v::AbstractVector)
Convert a BitVector into an Int.
source

# CompositionalNetworks.reduce_symbols — Function. 
julia
reduce_symbols(symbols, sep)
Produce a formatted string that separates the symbols by sep. Used internally for show_composition.
source

Missing docstring.
Missing docstring for CompositionalNeworks.tr_in. Check Documenter's build log for details.
Metrics ​
# CompositionalNetworks.hamming — Function. 
julia
hamming(x, X)
Compute the hamming distance of x over a collection of solutions X, i.e. the minimal number of variables to switch in xto reach a solution.
source

# CompositionalNetworks.minkowski — Function. 
julia
minkowski(x, X, p)
source

# CompositionalNetworks.manhattan — Function. 
julia
manhattan(x, X)
source

Missing docstring.
Missing docstring for weigths_bias. Check Documenter's build log for details.

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Julia Constraints
Appearance
ConstraintLearning.jl ​
Documentation for ConstraintLearning.jl.
# ConstraintLearning.ICNConfig — Type. 
julia
struct ICNConfig{O <: ICNOptimizer}
A structure to hold the metric and optimizer configurations used in learning the weights of an ICN.
source

# ConstraintLearning.ICNConfig — Method. 
julia
ICNConfig(; metric = :hamming, optimizer = ICNGeneticOptimizer())
Constructor for ICNConfig. Defaults to hamming metric using a genetic algorithm.
source

# ConstraintLearning.ICNGeneticOptimizer — Method. 
julia
ICNGeneticOptimizer(; kargs...)
Default constructor to learn an ICN through a Genetic Algorithm. Default kargs TBW.
source

# ConstraintLearning.ICNLocalSearchOptimizer — Type. 
julia
ICNLocalSearchOptimizer(options = LocalSearchSolvers.Options())
Default constructor to learn an ICN through a CBLS solver.
source

# ConstraintLearning.ICNOptimizer — Type. 
julia
const ICNOptimizer = CompositionalNetworks.AbstractOptimizer
An abstract type for optmizers defined to learn ICNs.
source

# ConstraintLearning.QUBOGradientOptimizer — Method. 
julia
QUBOGradientOptimizer(; kargs...)
A QUBO optimizer based on gradient descent. Defaults TBW
source

# ConstraintLearning.QUBOOptimizer — Type. 
julia
const QUBOOptimizer = QUBOConstraints.AbstractOptimizer
An abstract type for optimizers used to learn QUBO matrices from constraints.
source

# CompositionalNetworks.optimize! — Method. 
julia
CompositionalNetworks.optimize!(icn, solutions, non_sltns, dom_size, metric, optimizer::ICNGeneticOptimizer; parameters...)
Extends the optimize! method to ICNGeneticOptimizer.
source

# CompositionalNetworks.optimize! — Method. 
julia
CompositionalNetworks.optimize!(icn, solutions, non_sltns, dom_size, metric, optimizer::ICNLocalSearchOptimizer; parameters...)
Extends the optimize! method to ICNLocalSearchOptimizer.
source

# ConstraintLearning._optimize! — Method. 
julia
_optimize!(icn, X, X_sols; metric = hamming, pop_size = 200)
Optimize and set the weights of an ICN with a given set of configuration X and solutions X_sols.
source

# ConstraintLearning.domain_size — Method. 
julia
domain_size(ds::Number)
Extends the domain_size function when ds is number (for dispatch purposes).
source

# ConstraintLearning.generate_population — Method. 
julia
generate_population(icn, pop_size
Generate a pôpulation of weights (individuals) for the genetic algorithm weighting icn.
source

# ConstraintLearning.icn — Method. 
julia
icn(X,X̅; kargs..., parameters...)
TBW
source

# ConstraintLearning.loss — Method. 
julia
loss(x, y, Q)
Loss of the prediction given by Q, a training set y, and a given configuration x.
source

# ConstraintLearning.make_df — Method. 
julia
make_df(X, Q, penalty, binarization, domains)
DataFrame arrangement to output some basic evaluation of a matrix Q.
source

# ConstraintLearning.make_set_penalty — Method. 
julia
make_set_penalty(X, X̅, args...; kargs)
Return a penalty function when the training set is already split into a pair of solutions X and non solutions X̅.
source

# ConstraintLearning.make_training_sets — Method. 
julia
make_training_sets(X, penalty, args...)
Return a pair of solutions and non solutions sets based on X and penalty.
source

# ConstraintLearning.mutually_exclusive — Method. 
julia
mutually_exclusive(layer, w)
Constraint ensuring that w encode exclusive operations in layer.
source

# ConstraintLearning.no_empty_layer — Method. 
julia
no_empty_layer(x; X = nothing)
Constraint ensuring that at least one operation is selected.
source

# ConstraintLearning.optimize! — Method. 
julia
optimize!(icn, X, X_sols, global_iter, local_iter; metric=hamming, popSize=100)
Optimize and set the weights of an ICN with a given set of configuration X and solutions X_sols. The best weights among global_iter will be set.
source

# ConstraintLearning.parameter_specific_operations — Method. 
julia
parameter_specific_operations(x; X = nothing)
Constraint ensuring that at least one operation related to parameters is selected if the error function to be learned is parametric.
source

# ConstraintLearning.predict — Method. 
julia
predict(x, Q)
Return the predictions given by Q for a given configuration x.
source

# ConstraintLearning.preliminaries — Method. 
julia
preliminaries(args)
Preliminaries to the training process in a QUBOGradientOptimizer run.
source

# ConstraintLearning.qubo — Function. 
julia
qubo(X,X̅; kargs..., parameters...)
TBW
source

# ConstraintLearning.sub_eltype — Method. 
julia
sub_eltype(X)
Return the element type of of the first element of a collection.
source

# ConstraintLearning.train! — Method. 
julia
train!(Q, X, penalty, η, precision, X_test, oversampling, binarization, domains)
Training inner method.
source

# ConstraintLearning.train — Method. 
julia
train(X, penalty[, d]; optimizer = QUBOGradientOptimizer(), X_test = X)
Learn a QUBO matrix on training set X for a constraint defined by penalty with optional domain information d. By default, it uses a QUBOGradientOptimizer and X as a testing set.
source

# ConstraintLearning.δ — Method. 
julia
δ(X[, Y]; discrete = true)
Compute the extrema over a collection X``or a pair of collection(X, Y)`.
source


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Julia Constraints
Appearance
Learning about Constraints ​
About learning constraints related matters.
Parameters ​
<!– To be moved –>
One major challenge of learning the features of constraints is exploring the domains of parameters. To tackle this issue, we provide some way to generate parameters from variables' domains.
Missing docstring.
Missing docstring for BoolParameterDomain. Check Documenter's build log for details.
Missing docstring.
Missing docstring for DimParameterDomain. Check Documenter's build log for details.
Missing docstring.
Missing docstring for IdParameterDomain. Check Documenter's build log for details.
Missing docstring.
Missing docstring for FakeAutomaton. Check Documenter's build log for details.
# ConstraintCommons.accept — Function. 
julia
accept(a::Union{Automaton, MDD}, w)
Return true if a accepts the word w and false otherwise.
source
julia
ConstraintCommons.accept(fa::FakeAutomaton, word)
Implement the accept methods for FakeAutomaton.
source

Missing docstring.
Missing docstring for fake_automaton. Check Documenter's build log for details.
Missing docstring.
Missing docstring for LanguageParameterDomain. Check Documenter's build log for details.
Missing docstring.
Missing docstring for OpParameterDomain. Check Documenter's build log for details.
Missing docstring.
Missing docstring for PairVarsParameterDomain. Check Documenter's build log for details.
Missing docstring.
Missing docstring for ValParameterDomain. Check Documenter's build log for details.
Missing docstring.
Missing docstring for ValsParameterDomain. Check Documenter's build log for details.
# Base.rand — Function. 
julia
Base.rand(d::Union{Vector{D},Set{D}, D}) where {D<:AbstractDomain}
Extends Base.rand to (a collection of) domains.
source
julia
Base.rand(itv::Intervals)
 Base.rand(itv::Intervals, i)
Return a random value from itv, specifically from the ith interval if i is specified.
source
julia
Base.rand(d::D) where D <: DiscreteDomain
Draw randomly a point in d.
source
julia
Base.rand(fa::FakeAutomaton)
Extends Base.rand. Currently simply returns fa.
source

# ConstraintDomains.generate_parameters — Function. 
julia
generate_parameters(d<:AbstractDomain, param)
Generates random parameters based on the domain d and the kind of parameters param.
source


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diff --git a/previews/PR53/learning/layers.html b/previews/PR53/learning/layers.html
index 8ff1f79..e345a82 100644
--- a/previews/PR53/learning/layers.html
+++ b/previews/PR53/learning/layers.html
@@ -8,10 +8,10 @@
     
     
     
-    
+    
     
     
-    
+    
     
     
     
@@ -21,7 +21,7 @@
 is_viable(icn)
 is_viable(icn, w)

Assert if a pair of layer/icn and weights compose a viable pattern. If no weights are given with an icn, it will check the current internal value.

source


# CompositionalNetworks.generate_inclusive_operations — Function.
julia
generate_inclusive_operations(predicate, bits)
 generate_exclusive_operation(max_op_number)

Generates the operations (weights) of a layer with inclusive/exclusive operations.

source


# CompositionalNetworks.generate_exclusive_operation — Function.
julia
generate_exclusive_operation(max_op_number)

Generates the operations (weights) of a layer with exclusive operations.

source


Missing docstring.

Missing docstring for generate_weigths. Check Documenter's build log for details.

- + \ No newline at end of file diff --git a/previews/PR53/learning/qubo_constraints.html b/previews/PR53/learning/qubo_constraints.html index 9ae912e..9cc19f1 100644 --- a/previews/PR53/learning/qubo_constraints.html +++ b/previews/PR53/learning/qubo_constraints.html @@ -8,17 +8,17 @@ - + - +
Julia Constraints

Appearance

Introduction to QUBOConstraints.jl ​

Introduction to QUBOConstraints.jl.

Basic features ​

# QUBOConstraints.QUBO_base — Function.
julia
QUBO_base(n, weight = 1)

A basic QUBO matrix to ensure that binarized variables keep a valid encoding.

source


# QUBOConstraints.QUBO_linear_sum — Function.
julia
QUBO_linear_sum(n, σ)

One valid QUBO matrix given n variables and parameter σ for the linear sum constraint.

source


- + \ No newline at end of file diff --git a/previews/PR53/learning/qubo_encoding.html b/previews/PR53/learning/qubo_encoding.html index eb6348e..8fa0191 100644 --- a/previews/PR53/learning/qubo_encoding.html +++ b/previews/PR53/learning/qubo_encoding.html @@ -8,17 +8,17 @@ - + - +
Julia Constraints

Appearance

Encoding for QUBO programs ​

# QUBOConstraints.is_valid — Function.
julia
is_valid(x, encoding::Symbol = :none)

Check if x has a valid format for encoding.

For instance, if encoding == :one_hot, at most one bit of x can be set to 1.

source


# QUBOConstraints.binarize — Function.
julia
binarize(x[, domain]; binarization = :one_hot)

Binarize x following the binarization encoding. If x is a vector (instead of a number per say), domain is optional.

source


# QUBOConstraints.debinarize — Function.
julia
debinarize(x[, domain]; binarization = :one_hot)

Transform a binary vector into a number or a set of number. If domain is not given, it will compute a default value based on binarization and x.

source


- + \ No newline at end of file diff --git a/previews/PR53/learning/qubo_learning.html b/previews/PR53/learning/qubo_learning.html index ad75238..f635f21 100644 --- a/previews/PR53/learning/qubo_learning.html +++ b/previews/PR53/learning/qubo_learning.html @@ -8,10 +8,10 @@ - + - + @@ -136,7 +136,7 @@ ) return train(X, penalty, to_domains(X, dom_stuff); optimizer, X_test) end - + \ No newline at end of file diff --git a/previews/PR53/learning/transformation.html b/previews/PR53/learning/transformation.html index 880dc37..3aabe99 100644 --- a/previews/PR53/learning/transformation.html +++ b/previews/PR53/learning/transformation.html @@ -8,10 +8,10 @@ - + - + @@ -46,7 +46,7 @@ # Apply a count equal to parameter transformation count_eq_param_result = val_transforms[:count_eq_param](data, param)

source


# CompositionalNetworks.transformation_layer — Function.
julia
transformation_layer(param = Vector{Symbol}())

Generate the layer of transformations functions of the ICN. Iff param value is non empty, also includes all the related parametric transformations.

source


- + \ No newline at end of file diff --git a/previews/PR53/meta/meta_strategist.html b/previews/PR53/meta/meta_strategist.html index 628839f..3d0b61a 100644 --- a/previews/PR53/meta/meta_strategist.html +++ b/previews/PR53/meta/meta_strategist.html @@ -8,17 +8,17 @@ - + - +
Julia Constraints

Appearance

MetaStrategist.jl ​

Documentation for MetaStrategist.jl.

- + \ No newline at end of file diff --git a/previews/PR53/perf/api.html b/previews/PR53/perf/api.html index 736f580..d5d6714 100644 --- a/previews/PR53/perf/api.html +++ b/previews/PR53/perf/api.html @@ -8,10 +8,10 @@ - + - + @@ -26,7 +26,7 @@ v"0.1.1" v"0.1.3" v"0.1.2"

source


- + \ No newline at end of file diff --git a/previews/PR53/perf/benchmark_ext.html b/previews/PR53/perf/benchmark_ext.html index 824bead..144a385 100644 --- a/previews/PR53/perf/benchmark_ext.html +++ b/previews/PR53/perf/benchmark_ext.html @@ -8,10 +8,10 @@ - + - + @@ -35,7 +35,7 @@ :gcsample => BenchmarkTools.DEFAULT_PARAMETERS.gcsample :time_tolerance => BenchmarkTools.DEFAULT_PARAMETERS.time_tolerance :memory_tolerance => BenchmarkTools.DEFAULT_PARAMETERS.memory_tolerance - + \ No newline at end of file diff --git a/previews/PR53/perf/chairmarks_ext.html b/previews/PR53/perf/chairmarks_ext.html index 4b93ef5..046b5f8 100644 --- a/previews/PR53/perf/chairmarks_ext.html +++ b/previews/PR53/perf/chairmarks_ext.html @@ -8,10 +8,10 @@ - + - + @@ -31,7 +31,7 @@ :seconds => 1, :samples => nothing :gc => true - + \ No newline at end of file diff --git a/previews/PR53/perf/perf_checker.html b/previews/PR53/perf/perf_checker.html index 9f7af01..ee0b84c 100644 --- a/previews/PR53/perf/perf_checker.html +++ b/previews/PR53/perf/perf_checker.html @@ -8,10 +8,10 @@ - + - + @@ -30,7 +30,7 @@ # Visualization of the results using Makie checkres_to_scatterlines(results)

The config dictionary can take many options, depending on the backend.

Some of the commonly used options are:

  • :PATH => The path where to the default environment of julia when creating a new process.

  • :pkgs => A list of versions to test performance for. Its defined as the Tuple, (name::String, option::Symbol, versions::Vector{VersionNumber}, last_or_first::Bool) Can be given as follows:

    • name is the name of the package.

    • option is one of the 5 symbols:

      • :patches: last patch or first patch of a version

      • :breaking: last breaking or next breaking version

      • :major: previous or next major version

      • :minor: previous or next minor version

      • :custom: custom version numbers (provide any boolean value for last_or_first in this case as it doesn't matter)

    • versions: The input for the provided option

    • last_or_first: Input for the provided option

  • :tags => A list of tags (a vector of symbols) to easily tag performance tests.

  • :devops => Giving a custom input to Pkg.develop. Intended to be used to test performance of a local development branch of a pacakge with previous versions. Often can be used as simply as :devops => "MyPackageName"

  • :threads => An integer to select the number of threads to start Julia with.

Checkout the documentation of the other backends for more default options and the default values.

- + \ No newline at end of file diff --git a/previews/PR53/perf/perf_interface.html b/previews/PR53/perf/perf_interface.html index a368584..5e4ed63 100644 --- a/previews/PR53/perf/perf_interface.html +++ b/previews/PR53/perf/perf_interface.html @@ -8,10 +8,10 @@ - + - + @@ -44,7 +44,7 @@ allocs = [chair.samples[i].allocs for i in 1:l] return Table(times = times, gctimes = gctimes, bytes = bytes, allocs = allocs) end

There are also other functions that can be overloaded, mostly related to plotting but these are the basic functions to extend PerfChecker for a custom backend.

- + \ No newline at end of file diff --git a/previews/PR53/perf/tutorial.html b/previews/PR53/perf/tutorial.html index 1df9b7d..0e00a65 100644 --- a/previews/PR53/perf/tutorial.html +++ b/previews/PR53/perf/tutorial.html @@ -8,10 +8,10 @@ - + - + @@ -57,7 +57,7 @@ c2 = checkres_to_boxplots(x, Val(:chairmark); kwarg) save(joinpath(@__DIR__, "visuals", "chair_boxplots_$kwarg.png"), c2) end

d here is the configuration dictionary. x stores the results from performance testing

The code below the macro call is for plotting and storing the plots. It creates the visuals folder and stores the following plots in the folder:

Boxplots from Chairmarks for allocations:

chair_boxplots

Boxplots from Chairmarks for times:

chair_times

Evolution of different metrics across versions according to Chairmarks:

chair_evolution - + \ No newline at end of file diff --git a/previews/PR53/public_api.html b/previews/PR53/public_api.html index 3fec549..6f50532 100644 --- a/previews/PR53/public_api.html +++ b/previews/PR53/public_api.html @@ -8,10 +8,10 @@ - + - + @@ -31,7 +31,7 @@ nvars, dom_size, param=nothing, icn=ICN(nvars, dom_size, param), X, X_sols, global_iter=100, local_iter=100, metric=hamming, popSize=200 )

Create an ICN, optimize it, and return its composition.

source


# CompositionalNetworks.manhattan — Method.
julia
manhattan(x, X)

source


# CompositionalNetworks.minkowski — Method.
julia
minkowski(x, X, p)

source


# CompositionalNetworks.nbits — Method.
julia
nbits(icn)

Return the expected number of bits of a viable weight of an ICN.

source


# CompositionalNetworks.regularization — Method.
julia
regularization(icn)

Return the regularization value of an ICN weights, which is proportional to the normalized number of operations selected in the icn layers.

source


# CompositionalNetworks.show_layers — Method.
julia
show_layers(icn)

Return a formatted string with each layers in the icn.

source


# CompositionalNetworks.symbols — Method.
julia
symbols(c::Composition)

Output the composition as a layered collection of Symbols.

source


# CompositionalNetworks.transformation_layer — Function.
julia
transformation_layer(param = Vector{Symbol}())

Generate the layer of transformations functions of the ICN. Iff param value is non empty, also includes all the related parametric transformations.

source


# CompositionalNetworks.weights! — Method.
julia
weights!(icn, weights)

Set the weights of an ICN with a BitVector.

source


# CompositionalNetworks.weights — Method.
julia
weights(icn)

Access the current set of weights of an ICN.

source


# CompositionalNetworks.weights_bias — Method.
julia
weights_bias(x)

A metric that bias x towards operations with a lower bit. Do not affect the main metric.

source


# QUBOConstraints.QUBO_linear_sum — Method.
julia
QUBO_linear_sum(n, σ)

One valid QUBO matrix given n variables and parameter σ for the linear sum constraint.

source


# QUBOConstraints.binarize — Method.
julia
binarize(x[, domain]; binarization = :one_hot)

Binarize x following the binarization encoding. If x is a vector (instead of a number per say), domain is optional.

source


# QUBOConstraints.debinarize — Method.
julia
debinarize(x[, domain]; binarization = :one_hot)

Transform a binary vector into a number or a set of number. If domain is not given, it will compute a default value based on binarization and x.

source


# QUBOConstraints.is_valid — Function.
julia
is_valid(x, encoding::Symbol = :none)

Check if x has a valid format for encoding.

For instance, if encoding == :one_hot, at most one bit of x can be set to 1.

source


# QUBOConstraints.train — Method.
julia
train(args...)

Default train method for any AbstractOptimizer.

source


- + \ No newline at end of file diff --git a/previews/PR53/solvers/cbls.html b/previews/PR53/solvers/cbls.html index 5f28fa5..39f1ed0 100644 --- a/previews/PR53/solvers/cbls.html +++ b/previews/PR53/solvers/cbls.html @@ -8,10 +8,10 @@ - + - + @@ -25,7 +25,7 @@ # Generic use @objective(model, ScalarFunction(f, X))

source


# CBLS.Sum — Type.

Global constraint ensuring that the sum of the variables in x satisfies a given condition.

source


# CBLS.Supports — Type.

Global constraint ensuring that the tuple x matches a configuration listed within the support set pair_vars. This constraint is derived from the extension model, specifying that x must be one of the explicitly defined supported configurations: x ∈ pair_vars. It is utilized to directly declare the tuples that are valid and should be included in the solution space.

julia
@constraint(model, X in Supports(; pair_vars))

source


# Base.copy — Method.
julia
Base.copy(set::MOIError) = begin

DOCSTRING

source


# Base.copy — Method.
julia
Base.copy(set::MOIIntention)

Copy an intention set.

Arguments

  • set::MOIIntention: The intention set to be copied.

Returns

  • MOIIntention: A copy of the intention set.

source


# Base.copy — Method.
julia
Base.copy(set::DiscreteSet)

Copy a discrete set.

Arguments

  • set::DiscreteSet: The discrete set to be copied.

Returns

  • DiscreteSet: A copy of the discrete set.

source


# Base.copy — Method.
julia
Base.copy(op::F) where {F <: Function}

Copy a function.

Arguments

  • op::F: The function to be copied.

Returns

  • F: The copied function.

source


# Base.copy — Method.
julia
Base.copy(::Nothing)

Copy a Nothing value.

Arguments

  • ::Nothing: The Nothing value to be copied.

Returns

  • Nothing: The copied Nothing value.

source


# JuMP.build_variable — Method.
julia
JuMP.build_variable(::Function, info::JuMP.VariableInfo, set::T) where T <: MOI.AbstractScalarSet

Create a variable constrained by a scalar set.

Arguments

  • info::JuMP.VariableInfo: Information about the variable to be created.

  • set::T where T <: MOI.AbstractScalarSet: The set defining the constraints on the variable.

Returns

  • JuMP.VariableConstrainedOnCreation: A variable constrained by the specified set.

source


# JuMP.moi_set — Method.
julia
JuMP.moi_set(set::Intention, dim::Int) -> MOIIntention

Convert an Intention set to a MOIIntention set.

Arguments

  • set::Intention: The intention set to be converted.

  • dim::Int: The dimension of the vector set.

Returns

  • MOIIntention: The converted MOIIntention set.

source


# JuMP.moi_set — Method.
julia
JuMP.moi_set(set::Predicate, dim::Int) -> MOIIntention

Convert a Predicate set to a MOIIntention set.

Arguments

  • set::Predicate: The predicate set to be converted.

  • dim::Int: The dimension of the vector set.

Returns

  • MOIIntention: The converted MOIIntention set.

source


# MathOptInterface.add_constraint — Method.
julia
MOI.add_constraint(optimizer::Optimizer, vars::MOI.VectorOfVariables, set::MOIError)

DOCSTRING

Arguments:

  • optimizer: DESCRIPTION

  • vars: DESCRIPTION

  • set: DESCRIPTION

source


# MathOptInterface.add_constraint — Method.
julia
MOI.add_constraint(optimizer::Optimizer, vars::MOI.VectorOfVariables, set::MOIIntention{F}) where {F <: Function}

Add an intention constraint to the optimizer.

Arguments

  • optimizer::Optimizer: The optimizer instance.

  • vars::MOI.VectorOfVariables: The variables for the constraint.

  • set::MOIIntention{F}: The intention set defining the constraint.

Returns

  • CI{VOV, MOIIntention{F}}: The constraint index.

source


# MathOptInterface.add_constraint — Method.
julia
MOI.add_constraint(optimizer::Optimizer, v::VI, set::DiscreteSet{T}) where T <: Number

DOCSTRING

Arguments:

  • optimizer: DESCRIPTION

  • v: DESCRIPTION

  • set: DESCRIPTION

source


# MathOptInterface.add_variable — Method.
julia
MOI.add_variable(model::Optimizer) = begin

DOCSTRING

source


# MathOptInterface.copy_to — Method.
julia
MOI.copy_to(model::Optimizer, src::MOI.ModelLike)

Copy the source model to the optimizer.

Arguments

  • model::Optimizer: The optimizer instance.

  • src::MOI.ModelLike: The source model to be copied.

Returns

  • Nothing

source


# MathOptInterface.empty! — Method.
julia
MOI.empty!(opt)

Empty the optimizer.

Arguments

  • opt::Optimizer: The optimizer instance.

Returns

  • Nothing

source


# MathOptInterface.get — Method.
julia
MOI.get(::Optimizer, ::MOI.SolverName)

Get the name of the solver.

Arguments

  • ::Optimizer: The optimizer instance.

Returns

  • String: The name of the solver.

source


# MathOptInterface.get — Method.
julia
Moi.get(::Optimizer, ::MOI.SolverVersion)

Get the version of the solver, here LocalSearchSolvers.jl.

source


# MathOptInterface.is_empty — Method.
julia
MOI.is_empty(model::Optimizer)

Check if the model is empty.

Arguments

  • model::Optimizer: The optimizer instance.

Returns

  • Bool: True if the model is empty, false otherwise.

source


# MathOptInterface.is_valid — Method.
julia
MOI.is_valid(optimizer::Optimizer, index::CI{VI, MOI.Integer})

Check if an index is valid for the optimizer.

Arguments

  • optimizer::Optimizer: The optimizer instance.

  • index::CI{VI, MOI.Integer}: The index to be checked.

Returns

  • Bool: True if the index is valid, false otherwise.

source


# MathOptInterface.optimize! — Method.
julia
MOI.optimize!(model::Optimizer)

Optimize the model using the optimizer.

Arguments

  • model::Optimizer: The optimizer instance.

Returns

  • Nothing

source


# MathOptInterface.set — Function.
julia
MOI.set(::Optimizer, ::MOI.Silent, bool = true)

Set the verbosity of the solver.

Arguments

  • ::Optimizer: The optimizer instance.

  • ::MOI.Silent: The silent option for the solver.

  • bool::Bool: Whether to set the solver to silent mode.

Returns

  • Nothing

source


# MathOptInterface.set — Method.
julia
MOI.set(model::Optimizer, p::MOI.RawOptimizerAttribute, value)

Set a RawOptimizerAttribute to value

source


# MathOptInterface.set — Method.
julia
MOI.set(model::Optimizer, ::MOI.TimeLimitSec, value::Union{Nothing,Float64})

Set the time limit

source


# MathOptInterface.supports_constraint — Method.
julia
MOI.supports_constraint(::Optimizer, ::Type{VOV}, ::Type{MOIError}) = begin

DOCSTRING

Arguments:

  • ``: DESCRIPTION

  • ``: DESCRIPTION

  • ``: DESCRIPTION

source


# MathOptInterface.supports_constraint — Method.
julia
MOI.supports_constraint(::Optimizer, ::Type{VOV}, ::Type{MOIIntention{F}}) where {F <: Function}

Check if the optimizer supports a given intention constraint.

Arguments

  • ::Optimizer: The optimizer instance.

  • ::Type{VOV}: The type of the variable.

  • ::Type{MOIIntention{F}}: The type of the intention.

Returns

  • Bool: True if the optimizer supports the constraint, false otherwise.

source


# MathOptInterface.supports_incremental_interface — Method.
julia
MOI.supports_incremental_interface(::Optimizer)

Check if the optimizer supports incremental interface.

Arguments

  • ::Optimizer: The optimizer instance.

Returns

  • Bool: True if the optimizer supports incremental interface, false otherwise.

source


- + \ No newline at end of file diff --git a/previews/PR53/solvers/intro.html b/previews/PR53/solvers/intro.html index 9aaef1e..3f33e29 100644 --- a/previews/PR53/solvers/intro.html +++ b/previews/PR53/solvers/intro.html @@ -8,17 +8,17 @@ - + - +
Julia Constraints

Appearance

- + \ No newline at end of file diff --git a/previews/PR53/solvers/local_search_solvers.html b/previews/PR53/solvers/local_search_solvers.html index 8dffacb..f7d2078 100644 --- a/previews/PR53/solvers/local_search_solvers.html +++ b/previews/PR53/solvers/local_search_solvers.html @@ -8,10 +8,10 @@ - + - + @@ -53,7 +53,7 @@ variable(domain::AbstractDomain, name::AbstractString) where D <: AbstractDomain

Construct a variable with discrete domain. See the domain method for other options.

julia
d = domain([1,2,3,4], types = :indices)
 x1 = variable(d, "x1")
 x2 = variable([-89,56,28], "x2", domain = :indices)

source


- + \ No newline at end of file