Add posibility of calculating [value] gradient and hessian for Newton… (#137)

* Add posibility of calculating [value] gradient and hessian for NewtonTrustRegion efficiency.

* Impove gradient_hessiangit add src/ Project.toml .travis.yml .

Author: pkofod

.travis.yml

@@ -4,7 +4,7 @@ os:
   - linux
   - osx
 julia:
-  - 1.2
+  - 1
   - nightly
 notifications:
   email: false

Project.toml

@@ -12,7 +12,7 @@ ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 DiffResults = "1.0"
 ForwardDiff = "0.10"
 FiniteDiff = "2.0"
-julia = "1.2"
+julia = "1.5"
 
 [extras]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"

src/objective_types/incomplete.jl

@@ -93,23 +93,63 @@ end
 const InPlaceFGH = InplaceObjective{<:Nothing,<:Nothing,TH,<:Nothing,<:Nothing} where {TH}
 const InPlaceFG_HV = InplaceObjective{<:Nothing,TFG,<:Nothing,THv,<:Nothing} where {TFG,THv}
 const InPlaceFGHV = InplaceObjective{<:Nothing,<:Nothing,<:Nothing,<:Nothing,TFGHv} where {TFGHv}
-
 function TwiceDifferentiable(t::InPlaceFGH, x::AbstractArray, F::Real = real(zero(eltype(x))), G::AbstractArray = alloc_DF(x, F), H = alloc_H(x, F)) where {TH}
     f   =     x  -> t.fgh(F, nothing, nothing, x)
     df  = (G, x) -> t.fgh(nothing, G, nothing, x)
     fdf = (G, x) -> t.fgh(F, G, nothing, x)
+    fdfh = (G, H, x) -> t.fgh(F, G, H, x)
+    dfh = (G, H, x) -> t.fgh(nothing, G, H, x)
     h   = (H, x) -> t.fgh(F, nothing, H, x)
-    TwiceDifferentiable(f, df, fdf, h, x, F, G, H)
-end
 
-function TwiceDifferentiable(t::InPlaceFGH, x::AbstractVector, F::Real = real(zero(eltype(x))), G::AbstractVector = similar(x)) where {TH}
+    x_f, x_df, x_h = x_of_nans(x), x_of_nans(x), x_of_nans(x)
 
-    H = alloc_H(x, F)
+    TwiceDifferentiable(f, df, fdf, dfh, fdfh, h,
+                                        copy(F), copy(G), copy(H),
+                                        x_f, x_df, x_h,
+                                        [0,], [0,], [0,])
+end
+function TwiceDifferentiable(t::InPlaceFGH, x::AbstractVector{T}, F::Real = real(zero(eltype(x))), G::AbstractVector{Tx} = alloc_DF(x, F)) where {T, Tx}
     f   =     x  -> t.fgh(F, nothing, nothing, x)
     df  = (G, x) -> t.fgh(nothing, G, nothing, x)
     fdf = (G, x) -> t.fgh(F, G, nothing, x)
+    fdfh = (G, H, x) -> t.fgh(F, G, H, x)
+    dfh = (G, H, x) -> t.fgh(nothing, G, H, x)
     h   = (H, x) -> t.fgh(F, nothing, H, x)
-    TwiceDifferentiable(f, df, fdf, h, x, F, G, H)
+
+    H = alloc_H(x, F)
+    x_f, x_df, x_h = x_of_nans(x), x_of_nans(x), x_of_nans(x)
+
+    TwiceDifferentiable(f, df, fdf, dfh, fdfh, h,
+                                        copy(F), copy(G), copy(H),
+                                        x_f, x_df, x_h,
+                                        [0,], [0,], [0,])
+end
+function value_gradient_hessian!!(obj, x)
+    obj.f_calls .+= 1
+    obj.df_calls .+= 1
+    obj.h_calls .+= 1
+    obj.x_f  .= x
+    obj.x_df .= x
+    obj.x_h  .= x
+    if obj.fdfh === nothing
+        obj.F = obj.fdf(obj.DF, x)
+        obj.h(obj.H, x)
+    else
+        obj.F = obj.fdfh(obj.DF, obj.H, x)
+    end
+end
+
+function gradient_hessian!!(obj, x)
+    if obj.dfh === nothing
+        gradient!!(obj, x)
+        hessian!!(obj, x)
+    else
+        obj.df_calls .+= 1
+        obj.h_calls .+= 1
+        obj.x_df .= x
+        obj.x_h  .= x
+        obj.dfh(obj.DF, obj.H, x)
+    end
 end
 
 function TwiceDifferentiableHV(t::InPlaceFG_HV, x::AbstractVector)

src/objective_types/twicedifferentiable.jl

@@ -3,6 +3,8 @@ mutable struct TwiceDifferentiable{T,TDF,TH,TX} <: AbstractObjective
     f
     df
     fdf
+    dfh
+    fdfh
     h
     F::T
     DF::TDF
@@ -22,7 +24,7 @@ function TwiceDifferentiable(f, g, fg, h, x::TX, F::T = real(zero(eltype(x))), G
     fg! = fdf!_from_fdf(fg, F, inplace)
     h! = h!_from_h(h, F, inplace)
 
-    TwiceDifferentiable{T,TG,TH,TX}(f, g!, fg!, h!,
+    TwiceDifferentiable{T,TG,TH,TX}(f, g!, fg!, nothing, nothing, h!,
                                         copy(F), copy(G), copy(H),
                                         x_f, x_df, x_h,
                                         [0,], [0,], [0,])
@@ -39,7 +41,7 @@ function TwiceDifferentiable(f, g, h,
     fg! = make_fdf(x, F, f, g!)
     x_f, x_df, x_h = x_of_nans(x), x_of_nans(x), x_of_nans(x)
 
-    return TwiceDifferentiable(f, g!, fg!, h!, F, G, H, x_f, x_df, x_h, [0,], [0,], [0,])
+    return TwiceDifferentiable(f, g!, fg!, nothing, nothing, h!, F, G, H, x_f, x_df, x_h, [0,], [0,], [0,])
 end
 
 
@@ -98,7 +100,7 @@ function TwiceDifferentiable(d::OnceDifferentiable, x_seed::AbstractVector{T} =
     return TwiceDifferentiable(d.f, d.df, d.fdf, h!, x_seed, F, gradient(d))
 end
 
-function TwiceDifferentiable(f, x::AbstractVector, F::Real = real(zero(eltype(x)));
+function TwiceDifferentiable(f, x::AbstractArray, F::Real = real(zero(eltype(x)));
                              autodiff = :finite, inplace = true)
     if is_finitediff(autodiff)