From d6d530a21ec1aea4a26717d84d873f07b3bd5897 Mon Sep 17 00:00:00 2001
From: ArnoStrouwen <arno.strouwen@telenet.be>
Date: Sun, 13 Nov 2022 21:12:41 +0100
Subject: [PATCH] remove scalar->vector gradients

---
 src/gradients.jl        | 60 -----------------------------------------
 test/finitedifftests.jl | 49 ---------------------------------
 2 files changed, 109 deletions(-)

diff --git a/src/gradients.jl b/src/gradients.jl
index 3ed61fb..69cf5de 100644
--- a/src/gradients.jl
+++ b/src/gradients.jl
@@ -352,63 +352,3 @@ function finite_difference_gradient!(
     end
     df
 end
-
-# vector of derivatives of a scalar->vector map
-# this is effectively a vector of partial derivatives, but we still call it a gradient
-function finite_difference_gradient!(
-    df,
-    f,
-    x::Number,
-    cache::GradientCache{T1,T2,T3,T4,fdtype,returntype,inplace};
-    relstep=default_relstep(fdtype, eltype(x)),
-    absstep=relstep,
-    dir=true) where {T1,T2,T3,T4,fdtype,returntype,inplace}
-
-    # NOTE: in this case epsilon is a scalar, we need two arrays for fx1 and fx2
-    # c1 denotes fx1, c2 is fx2, sizes guaranteed by the cache constructor
-    fx, c1, c2 = cache.fx, cache.c1, cache.c2
-
-    if inplace == Val(true)
-        _c1, _c2 = c1, c2
-    end
-
-    if fdtype == Val(:forward)
-        epsilon = compute_epsilon(Val(:forward), x, relstep, absstep, dir)
-        if inplace == Val(true)
-            f(c1, x + epsilon)
-        else
-            _c1 = f(x + epsilon)
-        end
-        if typeof(fx) != Nothing
-            @. df = (_c1 - fx) / epsilon
-        else
-            if inplace == Val(true)
-                f(c2, x)
-            else
-                _c2 = f(x)
-            end
-            @. df = (_c1 - _c2) / epsilon
-        end
-    elseif fdtype == Val(:central)
-        epsilon = compute_epsilon(Val(:central), x, relstep, absstep, dir)
-        if inplace == Val(true)
-            f(c1, x + epsilon)
-            f(c2, x - epsilon)
-        else
-            _c1 = f(x + epsilon)
-            _c2 = f(x - epsilon)
-        end
-        @. df = (_c1 - _c2) / (2 * epsilon)
-    elseif fdtype == Val(:complex) && returntype <: Real
-        epsilon_complex = eps(real(eltype(x)))
-        if inplace == Val(true)
-            f(c1, x + im * epsilon_complex)
-        else
-            _c1 = f(x + im * epsilon_complex)
-        end
-        @. df = imag(_c1) / epsilon_complex
-    else
-        fdtype_error(returntype)
-    end
-    df
-end
diff --git a/test/finitedifftests.jl b/test/finitedifftests.jl
index 29ae9de..89b1b60 100644
--- a/test/finitedifftests.jl
+++ b/test/finitedifftests.jl
@@ -261,55 +261,6 @@ central_cache = FiniteDiff.GradientCache(df, x, Val{:central}, eltype(df))
     @test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, central_cache), df_ref) < 1e-8
 end
 
-f(df, x) = (df[1] = sin(x); df[2] = cos(x); df)
-z = x = 2π * rand()
-fx = fill(0.0, 2)
-f(fx, x)
-ff(df, x) = !all(x .<= z) ? error() : f(df, x)
-df = fill(0.0, 2)
-df_ref = [cos(x), -sin(x)]
-forward_cache = FiniteDiff.GradientCache(df, x, Val{:forward})
-central_cache = FiniteDiff.GradientCache(df, x, Val{:central})
-complex_cache = FiniteDiff.GradientCache(df, x, Val{:complex})
-
-
-@time @testset "Gradient of f:scalar->vector real-valued tests" begin
-    @test_broken err_func(FiniteDiff.finite_difference_gradient(f, x, Val{:forward}), df_ref) < 1e-4
-    @test err_func(FiniteDiff.finite_difference_gradient(f, x, Val{:forward}, eltype(x), Val{true}, fx), df_ref) < 1e-4
-    @test err_func(FiniteDiff.finite_difference_gradient(ff, x, Val{:forward}, eltype(x), Val{true}, fx, dir=-1), df_ref) < 1e-4
-    @test_throws Any err_func(FiniteDiff.finite_difference_gradient(ff, x, Val{:forward}), df_ref) < 1e-4
-    @test err_func(FiniteDiff.finite_difference_gradient(f, x, Val{:central}, eltype(x), Val{true}, fx), df_ref) < 1e-8
-    @test err_func(FiniteDiff.finite_difference_gradient(f, x, Val{:complex}, eltype(x), Val{true}, fx), df_ref) < 1e-15
-
-    @test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, Val{:forward}), df_ref) < 1e-4
-    @test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, Val{:central}), df_ref) < 1e-8
-    @test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, Val{:complex}), df_ref) < 1e-15
-
-    @test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, forward_cache), df_ref) < 1e-4
-    @test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, central_cache), df_ref) < 1e-8
-    @test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, complex_cache), df_ref) < 1e-15
-end
-
-f(df, x) = (df[1] = sin(x); df[2] = cos(x); df)
-x = (2π * rand()) * (1 + im)
-fx = fill(zero(typeof(x)), 2)
-f(fx, x)
-df = zero(fx)
-df_ref = [cos(x), -sin(x)]
-forward_cache = FiniteDiff.GradientCache(df, x, Val{:forward})
-central_cache = FiniteDiff.GradientCache(df, x, Val{:central})
-
-@time @testset "Gradient of f:vector->scalar complex-valued tests" begin
-    @test err_func(FiniteDiff.finite_difference_gradient(f, x, Val{:forward}, eltype(x), Val{true}, fx), df_ref) < 1e-4
-    @test err_func(FiniteDiff.finite_difference_gradient(f, x, Val{:central}, eltype(x), Val{true}, fx), df_ref) < 3e-7
-
-    @test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, Val{:forward}), df_ref) < 1e-4
-    @test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, Val{:central}), df_ref) < 3e-7
-
-    @test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, forward_cache), df_ref) < 1e-4
-    @test err_func(FiniteDiff.finite_difference_gradient!(df, f, x, central_cache), df_ref) < 3e-7
-end
-
 function ret_allocs(res, _f, x, cache)
     allocs = @allocated FiniteDiff.finite_difference_gradient!(res, _f, x, cache)
     allocs