remove f and c in structure

JuliaSmoothOptimizers · May 20, 2021 · 8da9956 · 8da9956
1 parent e9679e3
commit 8da9956
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Showing 7 changed files with 25 additions and 31 deletions.
diff --git a/src/ad.jl b/src/ad.jl
@@ -3,44 +3,38 @@ struct ForwardDiffAD <: ADBackend
   nnzh::Int
   nnzj::Int
 end
-function ForwardDiffAD(f, c, x0::AbstractVector, ncon::Integer)
-  nvar = length(x0)
+function ForwardDiffAD(nvar::Integer, ncon::Integer)
   nnzh = nvar * (nvar + 1) / 2
   nnzj = nvar * ncon
   return ForwardDiffAD(nnzh, nnzj)
 end
-function ForwardDiffAD(f, x0::AbstractVector)
-  nvar = length(x0)
+function ForwardDiffAD(nvar::Integer)
   nnzh = nvar * (nvar + 1) / 2
   return ForwardDiffAD(nnzh, 0)
 end
 struct ZygoteAD <: ADBackend
   nnzh::Int
   nnzj::Int
 end
-function ZygoteAD(f, c, x0::AbstractVector, ncon::Integer)
-  nvar = length(x0)
+function ZygoteAD(nvar::Integer, ncon::Integer)
   nnzh = nvar * (nvar + 1) / 2
   nnzj = nvar * ncon
   return ZygoteAD(nnzh, nnzj)
 end
-function ZygoteAD(f, x0::AbstractVector)
-  nvar = length(x0)
+function ZygoteAD(nvar::Integer)
   nnzh = nvar * (nvar + 1) / 2
   return ZygoteAD(nnzh, 0)
 end
 struct ReverseDiffAD <: ADBackend
   nnzh::Int
   nnzj::Int
 end
-function ReverseDiffAD(f, c, x0::AbstractVector, ncon::Integer)
-  nvar = length(x0)
+function ReverseDiffAD(nvar::Integer, ncon::Integer)
   nnzh = nvar * (nvar + 1) / 2
   nnzj = nvar * ncon
   return ReverseDiffAD(nnzh, nnzj)
 end
-function ReverseDiffAD(f, x0::AbstractVector)
-  nvar = length(x0)
+function ReverseDiffAD(nvar::Integer)
   nnzh = nvar * (nvar + 1) / 2
   return ReverseDiffAD(nnzh, 0)
 end
@@ -127,7 +121,7 @@ end
       return Zygote.jacobian(f, x)[1]
     end
     function hessian(b::ZygoteAD, f, x)
-      return jacobian(ForwardDiffAD(f, x), x -> gradient(b, f, x), x)
+      return jacobian(ForwardDiffAD(length(x)), x -> gradient(b, f, x), x)
     end
     function Jprod(::ZygoteAD, f, x, v)
       return vec(Zygote.jacobian(t -> f(x + t * v), 0)[1])

diff --git a/src/nlp.jl b/src/nlp.jl
@@ -39,7 +39,7 @@ function ADNLPModel(
   f,
   x0::AbstractVector{T};
   name::String = "Generic",
-  adbackend = ForwardDiffAD(f, x0),
+  adbackend = ForwardDiffAD(length(x0)),
 ) where {T}
   nvar = length(x0)
   @lencheck nvar x0
@@ -57,7 +57,7 @@ function ADNLPModel(
   lvar::AbstractVector,
   uvar::AbstractVector;
   name::String = "Generic",
-  adbackend = ForwardDiffAD(f, x0),
+  adbackend = ForwardDiffAD(length(x0)),
 ) where {T}
   nvar = length(x0)
   @lencheck nvar x0 lvar uvar
@@ -87,7 +87,7 @@ function ADNLPModel(
   y0::AbstractVector = fill!(similar(lcon), zero(T)),
   name::String = "Generic",
   lin::AbstractVector{<:Integer} = Int[],
-  adbackend = ForwardDiffAD(f, c, x0, length(lcon)),
+  adbackend = ForwardDiffAD(length(x0), length(lcon)),
 ) where {T}
   nvar = length(x0)
   ncon = length(lcon)
@@ -129,7 +129,7 @@ function ADNLPModel(
   y0::AbstractVector = fill!(similar(lcon), zero(T)),
   name::String = "Generic",
   lin::AbstractVector{<:Integer} = Int[],
-  adbackend = ForwardDiffAD(f, c, x0, length(lcon)),
+  adbackend = ForwardDiffAD(length(x0), length(lcon)),
 ) where {T}
   nvar = length(x0)
   ncon = length(lcon)

diff --git a/src/nls.jl b/src/nls.jl
@@ -46,7 +46,7 @@ function ADNLSModel(
   nequ::Integer;
   linequ::AbstractVector{<:Integer} = Int[],
   name::String = "Generic",
-  adbackend = ForwardDiffAD(F, x0),
+  adbackend = ForwardDiffAD(length(x0)),
 ) where {T}
   nvar = length(x0)
 
@@ -72,7 +72,7 @@ function ADNLSModel(
   uvar::AbstractVector;
   linequ::AbstractVector{<:Integer} = Int[],
   name::String = "Generic",
-  adbackend = ForwardDiffAD(F, x0),
+  adbackend = ForwardDiffAD(length(x0)),
 ) where {T}
   nvar = length(x0)
   @lencheck nvar lvar uvar
@@ -102,7 +102,7 @@ function ADNLSModel(
   lin::AbstractVector{<:Integer} = Int[],
   linequ::AbstractVector{<:Integer} = Int[],
   name::String = "Generic",
-  adbackend = ForwardDiffAD(F, c, x0, length(lcon)),
+  adbackend = ForwardDiffAD(length(x0), length(lcon)),
 ) where {T}
   nvar = length(x0)
   ncon = length(lcon)
@@ -148,7 +148,7 @@ function ADNLSModel(
   lin::AbstractVector{<:Integer} = Int[],
   linequ::AbstractVector{<:Integer} = Int[],
   name::String = "Generic",
-  adbackend = ForwardDiffAD(F, c, x0, length(lcon)),
+  adbackend = ForwardDiffAD(length(x0), length(lcon)),
 ) where {T}
   nvar = length(x0)
   ncon = length(lcon)

diff --git a/test/nlp/basic.jl b/test/nlp/basic.jl
@@ -25,25 +25,25 @@ function test_autodiff_model()
   for adbackend in (:ForwardDiffAD, :ZygoteAD, :ReverseDiffAD)
     x0 = zeros(2)
     f(x) = dot(x, x)
-    nlp = ADNLPModel(f, x0, adbackend = eval(adbackend)(f, x0))
+    nlp = ADNLPModel(f, x0, adbackend = eval(adbackend)(length(x0)))
 
     c(x) = [sum(x) - 1]
-    nlp = ADNLPModel(f, x0, c, [0], [0], adbackend = eval(adbackend)(f, c, x0, 1))
+    nlp = ADNLPModel(f, x0, c, [0], [0], adbackend = eval(adbackend)(length(x0), 1))
     @test obj(nlp, x0) == f(x0)
 
     x = range(-1, stop = 1, length = 100)
     y = 2x .+ 3 + randn(100) * 0.1
     regr = LinearRegression(x, y)
-    nlp = ADNLPModel(regr, ones(2), adbackend = eval(adbackend)(regr, ones(2)))
+    nlp = ADNLPModel(regr, ones(2), adbackend = eval(adbackend)(2))
     β = [ones(100) x] \ y
     @test abs(obj(nlp, β) - norm(y .- β[1] - β[2] * x)^2 / 2) < 1e-12
     @test norm(grad(nlp, β)) < 1e-12
 
     @testset "Constructors for ADNLPModel" begin
       lvar, uvar, lcon, ucon, y0 = -ones(2), ones(2), -ones(1), ones(1), zeros(1)
       badlvar, baduvar, badlcon, baducon, bady0 = -ones(3), ones(3), -ones(2), ones(2), zeros(2)
-      unc_adbackend = eval(adbackend)(f, x0)
-      con_adbackend = eval(adbackend)(f, c, x0, 1)
+      unc_adbackend = eval(adbackend)(2)
+      con_adbackend = eval(adbackend)(2, 1)
       nlp = ADNLPModel(f, x0, adbackend = unc_adbackend)
       nlp = ADNLPModel(f, x0, lvar, uvar, adbackend = unc_adbackend)
       nlp = ADNLPModel(f, x0, c, lcon, ucon, adbackend = con_adbackend)

diff --git a/test/nlp/nlpmodelstest.jl b/test/nlp/nlpmodelstest.jl
@@ -3,7 +3,7 @@
   for problem in NLPModelsTest.nlp_problems
     @testset "Checking NLPModelsTest tests on problem $problem" begin
       nlp_ad = eval(Meta.parse(lowercase(problem) * "_autodiff"))()
-      nlp_ad.adbackend = eval(adbackend)(nlp_ad.f, nlp_ad.meta.x0)
+      nlp_ad.adbackend = eval(adbackend)(length(nlp_ad.meta.x0))
       nlp_man = eval(Meta.parse(problem))()
 
       show(IOBuffer(), nlp_ad)

diff --git a/test/nls/basic.jl b/test/nls/basic.jl
@@ -2,7 +2,7 @@ function autodiff_nls_test()
   for adbackend in (:ForwardDiffAD, :ZygoteAD, :ReverseDiffAD)
     @testset "autodiff_nls_test for $adbackend" begin
       F(x) = [x[1] - 1; x[2] - x[1]^2]
-      nls = ADNLSModel(F, zeros(2), 2, adbackend = eval(adbackend)(F, zeros(2)))
+      nls = ADNLSModel(F, zeros(2), 2, adbackend = eval(adbackend)(2))
 
       @test isapprox(residual(nls, ones(2)), zeros(2), rtol = 1e-8)
     end
@@ -13,8 +13,8 @@ function autodiff_nls_test()
       c(x) = [sum(x) - 1]
       lvar, uvar, lcon, ucon, y0 = -ones(2), ones(2), -ones(1), ones(1), zeros(1)
       badlvar, baduvar, badlcon, baducon, bady0 = -ones(3), ones(3), -ones(2), ones(2), zeros(2)
-      unc_adbackend = eval(adbackend)(F, x0)
-      con_adbackend = eval(adbackend)(F, c, x0, 1)
+      unc_adbackend = eval(adbackend)(2)
+      con_adbackend = eval(adbackend)(2, 1)
       nlp = ADNLSModel(F, x0, 3, adbackend = unc_adbackend)
       nlp = ADNLSModel(F, x0, 3, lvar, uvar, adbackend = unc_adbackend)
       nlp = ADNLSModel(F, x0, 3, c, lcon, ucon, adbackend = con_adbackend)

diff --git a/test/nls/nlpmodelstest.jl b/test/nls/nlpmodelstest.jl
@@ -2,7 +2,7 @@
   for problem in NLPModelsTest.nls_problems
     @testset "Checking NLPModelsTest tests on problem $problem" begin
       nls_ad = eval(Meta.parse(lowercase(problem) * "_autodiff"))()
-      nls_ad.adbackend = eval(adbackend)(nls_ad.F, nls_ad.meta.x0)
+      nls_ad.adbackend = eval(adbackend)(length(nls_ad.meta.x0))
       nls_man = eval(Meta.parse(problem))()
 
       nlss = AbstractNLSModel[nls_ad]