Fix nlsolve

src/differentiable_vectors/autodiff.jl

@@ -1,4 +1,4 @@
-function OnceDifferentiable(f!, F::AbstractArray, x::AbstractArray, autodiff = :central)
+function OnceDifferentiable(f!, x::AbstractArray, F::AbstractArray, autodiff::Union{Symbol, Bool} = :central)
     if autodiff == :central
         function fj!(F, J, x)
             f!(F, x)
@@ -13,7 +13,7 @@ function OnceDifferentiable(f!, F::AbstractArray, x::AbstractArray, autodiff = :
             F = similar(x)
             fj!(F, J, x)
         end
-        return OnceDifferentiable(f!, j!, fj!, similar(x), similar(x))
+        return OnceDifferentiable(f!, j!, fj!, x, x)
     elseif autodiff == :forward || autodiff == true
         jac_cfg = ForwardDiff.JacobianConfig(f!, x, x)
         ForwardDiff.checktag(jac_cfg, f!, x)
@@ -28,7 +28,7 @@ function OnceDifferentiable(f!, F::AbstractArray, x::AbstractArray, autodiff = :
             DiffBase.value(jac_res)
         end
 
-        return OnceDifferentiable(f!, g!, fg!, similar(x), x)
+        return OnceDifferentiable(f!, g!, fg!, x, x)
     else
         error("The autodiff value $(autodiff) is not supported. Use :central or :forward.")
     end

src/nlsolve/nlsolve.jl

@@ -49,7 +49,7 @@ function nlsolve(f!,
                  autoscale::Bool = true,
                  m::Integer = 0,
                  beta::Real = 1.0) where T
-    nlsolve(OnceDifferentiable(f!, j!, similar(initial_x), initial_x),
+    nlsolve(OnceDifferentiable(f!, j!, initial_x, initial_x),
             initial_x, method = method, xtol = xtol, ftol = ftol,
             iterations = iterations, store_trace = store_trace,
             show_trace = show_trace, extended_trace = extended_trace,

src/solvers/anderson.jl

@@ -28,7 +28,7 @@
 
     for n = 1:iterations
         # fixed-point iteration
-        value!(df, fx, xs[:,1])
+        value!!(df, fx, xs[:,1])
 
         gs[:,1] .= xs[:,1] .+ β.*fx
 

src/solvers/mcp.jl

@@ -16,7 +16,7 @@ function mcp_smooth(df::OnceDifferentiable,
                     lower::Vector, upper::Vector)
 
     function f!(F, x)
-        value!(df, F, x)
+        value!!(df, F, x)
         for i = 1:length(x)
             if  isfinite.(upper[i])
                 F[i] += (x[i]-upper[i]) + sqrt(F[i]^2+(x[i]-upper[i])^2)
@@ -29,7 +29,7 @@ function mcp_smooth(df::OnceDifferentiable,
 
     function j!(J, x)
         F = similar(x)
-        value_jacobian!(df, F, J, x)
+        value_jacobian!!(df, F, J, x)
 
         # Derivatives of phiplus
         sqplus = sqrt.(F.^2 .+ (x .- upper).^2)
@@ -74,7 +74,7 @@ function mcp_smooth(df::OnceDifferentiable,
             J[i,i] += dminus_dv[i] + dminus_du[i]*dplus_dv[i]
         end
     end
-    return OnceDifferentiable(f!, j!, similar(df.F), similar(df.x_f))
+    return OnceDifferentiable(f!, j!, similar(df.x_f), similar(df.F))
 end
 
 # Generate a function whose roots are the solutions of the MCP.
@@ -87,7 +87,7 @@ end
 function mcp_minmax(df::OnceDifferentiable,
                     lower::Vector, upper::Vector)
     function f!(F, x)
-        value!(df, F, x)
+        value!!(df, F, x)
         for i = 1:length(x)
             if F[i] < x[i]-upper[i]
                 F[i] = x[i]-upper[i]
@@ -100,7 +100,7 @@ function mcp_minmax(df::OnceDifferentiable,
 
     function j!(J, x)
         F = similar(x)
-        value_jacobian!(df, F, J, x)
+        value_jacobian!!(df, F, J, x)
         for i = 1:length(x)
             if F[i] < x[i]-upper[i] || F[i] > x[i]-lower[i]
                 for j = 1:length(x)
@@ -109,5 +109,5 @@ function mcp_minmax(df::OnceDifferentiable,
             end
         end
     end
-    return OnceDifferentiable(f!, j!, similar(df.F), similar(df.x_f))
+    return OnceDifferentiable(f!, j!, similar(df.x_f), similar(df.F))
 end

src/solvers/mcp_func_defs.jl

@@ -52,7 +52,7 @@ function mcpsolve{T}(f!,
                   linesearch! = LineSearches.BackTracking(),
                   factor::Real = one(T),
                   autoscale::Bool = true)
-    @reformulate OnceDifferentiable(f!, j!, similar(initial_x), initial_x)
+    @reformulate OnceDifferentiable(f!, j!, initial_x, initial_x)
     nlsolve(rf,
             initial_x, method = method, xtol = xtol, ftol = ftol,
             iterations = iterations, store_trace = store_trace,

src/solvers/newton.jl

@@ -85,7 +85,7 @@ function newton_{T}(df::OnceDifferentiable,
         go!(storage, xlin)
         vecdot(value(df), value(df)) / 2
     end
-    dfo = OnceDifferentiable(fo, go!, fgo!, real(zero(T)), x)
+    dfo = OnceDifferentiable(fo, go!, fgo!, x, real(zero(T)))
 
     while !converged && it < iterations
 

test/minpack.jl

@@ -535,7 +535,7 @@ for (df, initial, name) in alltests
                 r.f_calls, r.g_calls, r.residual_norm, tot_time)
         @test converged(r)
         # with autodiff
-        tot_time = @elapsed r_AD = nlsolve(df.f, initial, method = method, autodiff = true)
+        tot_time2 = @elapsed r_AD = nlsolve(df.f, initial, method = method, autodiff = true)
         @printf("%-45s   %5d   %5d   %5d   %14e   %10e\n", name*"-"*string(method)*"-AD",
                 length(initial), r_AD.f_calls, r_AD.g_calls, r_AD.residual_norm, tot_time)
         if PRINT_FILE

test/sparse.jl

@@ -16,7 +16,7 @@ function g_sparse!(J, x)
     J[2, 2] = u
 end
 
-df = OnceDifferentiable(f_sparse!, g_sparse!, rand(2), spzeros(2, 2), rand(2))
+df = OnceDifferentiable(f_sparse!, g_sparse!, rand(2), rand(2), spzeros(2, 2))
 
 # Test trust region
 r = nlsolve(df, [ -0.5; 1.4], method = :trust_region, autoscale = true)
@@ -39,7 +39,7 @@ r = mcpsolve(df, [-Inf;-Inf], [Inf; Inf], [-0.5; 1.4], reformulation = :minmax)
 @test norm(r.zero - [ 0; 1]) < 1e-8
 
 # Test given sparse
-df = OnceDifferentiable(f_sparse!, g_sparse!, rand(2), spzeros(2, 2), rand(2))
+df = OnceDifferentiable(f_sparse!, g_sparse!, rand(2), rand(2), spzeros(2, 2))
 r = nlsolve(df, [ -0.5; 1.4], method = :trust_region, autoscale = true)
 @test converged(r)
 @test norm(r.zero - [ 0; 1]) < 1e-8