WIP: Remove add_tiny and add estimate_magitude

Project.toml

@@ -1,6 +1,6 @@
 name = "FiniteDifferences"
 uuid = "26cc04aa-876d-5657-8c51-4c34ba976000"
-version = "0.11.4"
+version = "0.11.5"
 
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"

src/methods.jl

@@ -1,13 +1,33 @@
 export FiniteDifferenceMethod, fdm, backward_fdm, forward_fdm, central_fdm, extrapolate_fdm
 
 """
-    add_tiny(x::Union{AbstractFloat, Integer})
+    estimate_magitude(f, x::T) where T<:AbstractFloat
 
-Add a tiny number, 10^{-40}, to a real floating point number `x`, preserving the type. If
-`x` is an `Integer`, it is promoted to a suitable floating point type.
+Estimate the magnitude of `f` in a neighbourhood of `x`, assuming that the outputs of `f`
+have a "typical" order of magnitude. The result should be interpreted as a very rough
+estimate. This function deals with the case that `f(x) = 0`.
 """
-add_tiny(x::T) where T<:AbstractFloat = x + convert(T, 1e-40)
-add_tiny(x::Integer) = add_tiny(float(x))
+function estimate_magitude(f, x::T) where T<:AbstractFloat
+    M = float(maximum(abs, f(x)))
+    M > 0 && (return M)
+    # Ouch, `f(x) = 0`. But it may not be zero around `x`. We conclude that `x` is likely a
+    # pathological input for `f`. Perturb `x`. Assume that the pertubed value for `x` is
+    # highly unlikely also a pathological value for `f`.
+    Δ = convert(T, 0.1) * max(abs(x), one(x))
+    return float(maximum(abs, f(x + Δ)))
+end
+
+"""
+    estimate_roundoff_error(f, x::T) where T<:AbstractFloat
+
+Estimate the round-off error of `f(x)`. This function deals with the case that `f(x) = 0`.
+"""
+function estimate_roundoff_error(f, x::T) where T<:AbstractFloat
+    # Estimate the round-off error. It can happen that the function is zero around `x`, in
+    # which case we cannot take `eps(f(x))`. Therefore, we assume a lower bound that is
+    # equal to `eps(T) / 1000`, which gives `f` four orders of magnitude wiggle room.
+    return max(eps(estimate_magitude(f, x)), eps(T) / 1000)
+end
 
 """
     FiniteDifferences.DEFAULT_CONDITION
@@ -209,7 +229,7 @@ end
 
 # Estimate the bound on the derivative by amplifying the ∞-norm.
 function _make_default_bound_estimator(; condition::Real=DEFAULT_CONDITION)
-    default_bound_estimator(f, x) = condition * maximum(abs, f(x))
+    default_bound_estimator(f, x) = condition * estimate_magitude(f, x)
     return default_bound_estimator
 end
 
@@ -256,17 +276,18 @@ function estimate_step(
 ) where T<:AbstractFloat
     p = length(m.coefs)
     q = m.q
-    f_x = float(f(x))
 
-    # Estimate the bound and round-off error.
-    ε = add_tiny(maximum(eps, f_x)) * factor
-    M = add_tiny(m.bound_estimator(f, x))
+    # Estimate the round-off error.
+    ε = estimate_roundoff_error(f, x) * factor
+
+    # Estimate the bound on the derivatives.
+    M = m.bound_estimator(f, x)
 
     # Set the step size by minimising an upper bound on the error of the estimate.
     C₁ = ε * sum(abs, m.coefs)
     C₂ = M * sum(n -> abs(m.coefs[n] * m.grid[n]^p), eachindex(m.coefs)) / factorial(p)
     # Type inference fails on this, so we annotate it, which gives big performance benefits.
-    h::T = convert(T, min((q / (p - q) * C₁ / C₂)^(1 / p), max_step))
+    h::T = convert(T, min((q / (p - q) * (C₁ / C₂))^(1 / p), max_step))
 
     # Estimate the accuracy of the method.
     accuracy = h^(-q) * C₁ + h^(p - q) * C₂
@@ -292,7 +313,7 @@ for direction in [:forward, :central, :backward]
                 grid,
                 q,
                 coefs,
-                _make_adaptive_bound_estimator($fdm_fun, p, adapt, condition, geom=geom),
+                _make_adaptive_bound_estimator($fdm_fun, p, q, adapt, condition, geom=geom),
             )
         end
 
@@ -327,16 +348,17 @@ end
 
 function _make_adaptive_bound_estimator(
     constructor::Function,
+    p::Int,
     q::Int,
     adapt::Int,
     condition::Int;
     kw_args...
 )
     if adapt >= 1
         estimate_derivative = constructor(
-            q + 1, q, adapt=adapt - 1, condition=condition; kw_args...
+            p + 1, p, adapt=adapt - 1, condition=condition; kw_args...
         )
-        return (f, x) -> maximum(abs, estimate_derivative(f, x))
+        return (f, x) -> estimate_magitude(x′ -> estimate_derivative(f, x′), x)
     else
         return _make_default_bound_estimator(condition=condition)
     end

test/methods.jl

@@ -1,15 +1,37 @@
-using FiniteDifferences: add_tiny
+import FiniteDifferences: estimate_magitude, estimate_roundoff_error
 
 @testset "Methods" begin
+    @testset "estimate_magitude" begin
+        f64(x::Float64) = x
+        f64_int(x::Float64) = Int(10x)
+        @test estimate_magitude(f64, 0.0) === 0.1
+        @test estimate_magitude(f64, 1.0) === 1.0
+        @test estimate_magitude(f64_int, 0.0) === 1.0
+        @test estimate_magitude(f64_int, 1.0) === 10.0
+
+        f32(x::Float32) = x
+        f32_int(x::Float32) = Int(10 * x)
+        @test estimate_magitude(f32, 0f0) === 0.1f0
+        @test estimate_magitude(f32, 1f0) === 1f0
+        # In this case, the `Int` is converted with `float`, so we expect a `Float64`.
+        @test estimate_magitude(f32_int, 0f0) === 1.0
+        @test estimate_magitude(f32_int, 1f0) === 10.0
+    end
+
+    @testset "estimate_roundoff_error" begin
+        # `Float64`s:
+        @test estimate_roundoff_error(identity, 1.0) == eps(1.0)
+        #   Pertubation from `estimate_magitude`:
+        @test estimate_roundoff_error(identity, 0.0) == eps(0.1)
 
-    @testset "add_tiny" begin
-        @test add_tiny(convert(Float64, 5)) isa Float64
-        @test add_tiny(convert(Float32, 5)) isa Float32
-        @test add_tiny(convert(Float16, 5)) isa Float16
+        # `Float32`s:
+        @test estimate_roundoff_error(identity, 1f0) == eps(1f0)
+        #   Pertubation from `estimate_magitude`:
+        @test estimate_roundoff_error(identity, 0.0f0) == eps(0.1f0)
 
-        @test add_tiny(convert(Int, 5)) isa Float64
-        @test add_tiny(convert(UInt, 5)) isa Float64
-        @test add_tiny(convert(Bool, 1)) isa Float64
+        # Test lower bound of `eps(T) / 1000`.
+        @test estimate_roundoff_error(x -> 1e-100, 0.0) == eps(1.0) / 1000
+        @test estimate_roundoff_error(x -> 1f-100, 0f0) == eps(1f0) / 1000
     end
 
     # The different approaches to approximating the gradient to try.
@@ -131,4 +153,13 @@ using FiniteDifferences: add_tiny
             end
         end
     end
+
+    @testset "Derivative of cosc at 0 (#124)" begin
+        @test central_fdm(5, 1)(cosc, 0) ≈ -(pi ^ 2) / 3 atol=1e-9
+        @test central_fdm(10, 1, adapt=3)(cosc, 0) ≈ -(pi ^ 2) / 3 atol=5e-14
+    end
+
+    @testset "Derivative of a constant (#125)" begin
+        @test central_fdm(2, 1)(x -> 0, 0) ≈ 0 atol=1e-10
+    end
 end