Merge pull request #172 from invenia/aa/fdm

Updates for FDM v0.6.0

Project.toml

@@ -1,6 +1,6 @@
 name = "Nabla"
 uuid = "49c96f43-aa6d-5a04-a506-44c7070ebe78"
-version = "0.12.0"
+version = "0.12.1"
 
 [deps]
 DiffRules = "b552c78f-8df3-52c6-915a-8e097449b14b"
@@ -13,7 +13,7 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 [compat]
 DiffRules = "^0.0"
 DualNumbers = ">=0.6.0"
-FDM = ">=0.1.0"
+FDM = "^0.6"
 SpecialFunctions = ">=0.5.0"
 julia = "^1.0"
 

src/finite_differencing.jl

@@ -21,9 +21,8 @@ function approximate_Dv(
     x::Tuple{Vararg{∇ArrayOrScalar}},
     v::Tuple{Vararg{∇ArrayOrScalar}}
 )
-    return central_5_1(ε -> sum(ȳ .* f((x .+ ε .* v)...)))
+    return FDM.Central(5, 1)(ε -> sum(ȳ .* f((x .+ ε .* v)...)))
 end
-central_5_1 = central_fdm(5, 1; M=5e8)
 approximate_Dv(f, ȳ::∇ArrayOrScalar, x::∇ArrayOrScalar, v::∇ArrayOrScalar) =
     approximate_Dv(f, ȳ, (x,), (v,))
 

test/sensitivities/linalg/blas.jl

@@ -27,6 +27,7 @@ using LinearAlgebra.BLAS
         λ = x->asum(50, x, 2)
         for _ in 1:10
             x, vx = randn(rng, 100), randn(rng, 100)
+            x .+= sign.(x)
             @test check_errs(asum, randn(rng), x, vx)
             @test check_errs(λ, randn(rng), x, vx)
         end

test/sensitivities/linalg/factorization/cholesky.jl

@@ -46,9 +46,9 @@
     let rng = MersenneTwister(123456), N = 10
         for _ in 1:10
             B, VB = randn.(Ref(rng), [N, N], [N, N])
-            A, VA = B'B + 1e-6I, VB'VB + 1e-6I
+            A, VA = B'B + I, VB'VB + I
             Ū = UpperTriangular(randn(rng, N, N))
-            @test check_errs(X->cholesky(X).U, Ū, A, 1e-2 .* VA)
+            @test check_errs(X->cholesky(X).U, Ū, A, VA)
         end
     end