diff --git a/docs/src/constraints/constraint_commons.md b/docs/src/constraints/constraint_commons.md index b62f42e..7d7fead 100644 --- a/docs/src/constraints/constraint_commons.md +++ b/docs/src/constraints/constraint_commons.md @@ -60,7 +60,7 @@ at_end ## Extensions -We extended some operations for `Nothing` and `Symbol`. +We extend some operations for `Nothing` and `Symbol`. ```@docs; canonical=false symcon @@ -89,7 +89,7 @@ consisempty ## Sampling -During our constraint learning processes, we use sampling to efficiently make partial exploration of search spaces. Follows some sampling utilities. +During our constraint learning processes, we use sampling to efficiently make partial exploration of search spaces. The following are some examples of sampling utilities. ```@docs; canonical=false oversample @@ -112,6 +112,8 @@ We need to compute the difference between extrema of various kind of collections δ_extrema ``` +### Performances + ```@raw html
Bench Evolution @@ -119,11 +121,9 @@ We need to compute the difference between extrema of various kind of collections
``` -### Performances - ## Dictionaries -We provide the everuseful `incsert!` function for dictionaries. +We provide the ever-useful `incsert!` function for dictionaries. ```@docs; canonical=false incsert! @@ -136,4 +136,4 @@ incsert! Bench Evolution Chair Evolution -``` \ No newline at end of file +``` diff --git a/docs/src/constraints/generic_constraints.md b/docs/src/constraints/generic_constraints.md index 51554d0..97a030e 100644 --- a/docs/src/constraints/generic_constraints.md +++ b/docs/src/constraints/generic_constraints.md @@ -14,7 +14,7 @@ Note that the *intention* constraint is not directly available through the JC-AP 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 language such as `JuMP` should provide a `Intention` interface. +Higher level modeling languages such as `JuMP` should provide a `Intention` interface. ### Defining an intention constraint in JC-API @@ -33,7 +33,7 @@ Ensures that the distances between marks on the ruler are unique. 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 ) @@ -44,11 +44,11 @@ Please check the section dedicated to the Golomb Ruler problem to see a use for ### 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. +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 `Intension` interface. +Higher level modeling language such as `JuMP` should provide an `Intention` interface. ::: code-group @@ -63,7 +63,7 @@ concept(:dist_different)(x) # 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]) ) @@ -76,35 +76,100 @@ c([1, 2, 3, 4]) # false 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) - -# Note that this example gives a solution for the constraint within the interval 0:10 +@info value.(Y) ``` ```julia [MOI] -# TODO: How to handle intention in JuMP/MOI -``` +using CBLS +import MathOptInterface as MOI -::: +optimizer = CBLS.Optimizer() -### Specific documentation +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]))) -```@docs; canonical=false -xcsp_intension +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 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](https://arxiv.org/abs/2009.00514) in Constraints.jl +::: code-group + +```julia [JC-API] +using Constraints -```@docs; canonical=false -xcsp_extension +concept(:dist_different, x) +concept(:dist_different)(x) ``` + +```julia [XCSP] +# 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 [JuMP] +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 [MOI] +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) +``` + +::: diff --git a/docs/src/constraints/variables_and_domains.md b/docs/src/constraints/variables_and_domains.md index ad8b223..a69f52c 100644 --- a/docs/src/constraints/variables_and_domains.md +++ b/docs/src/constraints/variables_and_domains.md @@ -69,7 +69,7 @@ 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 non-extremal element is removed). +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). ::: code-group