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JuliaNLSolvers / NLSolversBase.jl

Base package for optimization and equation solver software in JuliaNLSolvers
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Incorrect initialization of derivative storage #97

Closed andreasnoack closed 4 years ago

andreasnoack commented 5 years ago

In https://github.com/JuliaNLSolvers/NLSolversBase.jl/blob/12ceb15eb3c9ebc170f6d711ae73469a6b410e76/src/objective_types/twicedifferentiable.jl#L18, similar(x) is used for initialization of the gradient array but that need not be the right element type. In a nested derivative computation such as

julia> myf(x,y) = x^2+y^2
myf (generic function with 1 method)

julia> ForwardDiff.gradient(s -> optimize(t -> myf(t[1], s[1]), [0.0], Newton(), autodiff=:forward).minimum, [0.4])
ERROR: MethodError: no method matching Float64(::ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1})
Closest candidates are:
  Float64(::Real, ::RoundingMode) where T<:AbstractFloat at rounding.jl:194
  Float64(::T<:Number) where T<:Number at boot.jl:741
  Float64(::Int8) at float.jl:60
  ...
Stacktrace:
 [1] convert(::Type{Float64}, ::ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1}) at ./number.jl:7
 [2] setproperty!(::DiffResults.MutableDiffResult{1,Float64,Tuple{Array{Float64,1}}}, ::Symbol, ::ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1}) at ./sysimg.jl:19
 [3] value!(::DiffResults.MutableDiffResult{1,Float64,Tuple{Array{Float64,1}}}, ::ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1}) at /Users/andreasnoack/.julia/packages/DiffResults/1LURj/src/DiffResults.jl:159
 [4] extract_gradient!(::Type{ForwardDiff.Tag{getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},Float64}}, ::DiffResults.MutableDiffResult{1,Float64,Tuple{Array{Float64,1}}}, ::ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},Float64},ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}) at /Users/andreasnoack/.julia/packages/ForwardDiff/okZnq/src/gradient.jl:70
 [5] vector_mode_gradient!(::DiffResults.MutableDiffResult{1,Float64,Tuple{Array{Float64,1}}}, ::getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}}, ::Array{Float64,1}, ::ForwardDiff.GradientConfig{ForwardDiff.Tag{getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},Float64},Float64,1,Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},Float64},Float64,1},1}}) at /Users/andreasnoack/.julia/packages/ForwardDiff/okZnq/src/gradient.jl:103
 [6] gradient!(::DiffResults.MutableDiffResult{1,Float64,Tuple{Array{Float64,1}}}, ::getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}}, ::Array{Float64,1}, ::ForwardDiff.GradientConfig{ForwardDiff.Tag{getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},Float64},Float64,1,Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},Float64},Float64,1},1}}, ::Val{true}) at /Users/andreasnoack/.julia/packages/ForwardDiff/okZnq/src/gradient.jl:35
 [7] gradient!(::DiffResults.MutableDiffResult{1,Float64,Tuple{Array{Float64,1}}}, ::getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}}, ::Array{Float64,1}, ::ForwardDiff.GradientConfig{ForwardDiff.Tag{getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},Float64},Float64,1,Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},Float64},Float64,1},1}}) at /Users/andreasnoack/.julia/packages/ForwardDiff/okZnq/src/gradient.jl:33
 [8] (::getfield(NLSolversBase, Symbol("##40#46")){getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},ForwardDiff.GradientConfig{ForwardDiff.Tag{getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},Float64},Float64,1,Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##38#40")){Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}},Float64},Float64,1},1}}})(::Array{Float64,1}, ::Array{Float64,1}) at /Users/andreasnoack/.julia/dev/NLSolversBase/src/objective_types/twicedifferentiable.jl:122
 [9] value_gradient!!(::TwiceDifferentiable{Float64,Array{Float64,1},Array{Float64,2},Array{Float64,1}}, ::Array{Float64,1}) at /Users/andreasnoack/.julia/dev/NLSolversBase/src/interface.jl:82
 [10] initial_state(::Newton{LineSearches.InitialStatic{Float64},LineSearches.HagerZhang{Float64,Base.RefValue{Bool}}}, ::Optim.Options{Float64,Nothing}, ::TwiceDifferentiable{Float64,Array{Float64,1},Array{Float64,2},Array{Float64,1}}, ::Array{Float64,1}) at /Users/andreasnoack/.julia/dev/Optim/src/multivariate/solvers/second_order/newton.jl:45
 [11] #optimize#87 at /Users/andreasnoack/.julia/dev/Optim/src/multivariate/optimize/optimize.jl:33 [inlined]
 [12] (::getfield(Optim, Symbol("#kw##optimize")))(::NamedTuple{(:autodiff,),Tuple{Symbol}}, ::typeof(optimize), ::Function, ::Array{Float64,1}, ::Newton{LineSearches.InitialStatic{Float64},LineSearches.HagerZhang{Float64,Base.RefValue{Bool}}}, ::Optim.Options{Float64,Nothing}) at ./none:0 (repeats 2 times)
 [13] vector_mode_gradient(::getfield(Main, Symbol("##37#39")), ::Array{Float64,1}, ::ForwardDiff.GradientConfig{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1,Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}}) at ./none:1
 [14] gradient(::Function, ::Array{Float64,1}, ::ForwardDiff.GradientConfig{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1,Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}}, ::Val{true}) at /Users/andreasnoack/.julia/packages/ForwardDiff/okZnq/src/gradient.jl:17
 [15] gradient(::Function, ::Array{Float64,1}, ::ForwardDiff.GradientConfig{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1,Array{ForwardDiff.Dual{ForwardDiff.Tag{getfield(Main, Symbol("##37#39")),Float64},Float64,1},1}}) at /Users/andreasnoack/.julia/packages/ForwardDiff/okZnq/src/gradient.jl:15 (repeats 2 times)
 [16] top-level scope at none:0

the gradient will be a vector of duals which therefore causes an error.

mfherbst commented 4 years ago

Still failing for me on NLSolversBase v7.6.0, Optim v0.20.0 and ForwardDiff v0.10.9.

pkofod commented 4 years ago

Still failing for me on NLSolversBase v7.6.0, Optim v0.20.0 and ForwardDiff v0.10.9.

Sorry, I was not clear here, the "fix" was simply that you can supply the types yourself, but it still defaults to the old behavior. Although, as you showed, it didn't really for complex gradients :)

mfherbst commented 4 years ago

Ah ... I see. Thanks for the clarification.