Fix AD for NewtonTrustRegion. (#264)

JuliaNLSolvers · Aug 16, 2016 · fffb8ca · fffb8ca
1 parent 6d37caa
commit fffb8ca
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Showing 3 changed files with 12 additions and 16 deletions.
diff --git a/src/newton.jl b/src/newton.jl
@@ -28,13 +28,6 @@ end
 Newton(; linesearch!::Function = hz_linesearch!) =
   Newton(linesearch!)
 
-function optimize(d::Function,
-                  initial_x::Array,
-                  method::Newton,
-                  options::OptimizationOptions)
-    throw(ArgumentError("Newton's method without a user supplied gradient and hessian is currently not supported."))
-end
-
 function optimize{T}(d::TwiceDifferentiableFunction,
                      initial_x::Vector{T},
                      mo::Newton,

diff --git a/src/optimize.jl b/src/optimize.jl
@@ -150,7 +150,7 @@ end
 
 function optimize{T}(f::Function,
                   initial_x::Array{T},
-                  method::Newton,
+                  method::Union{Newton, NewtonTrustRegion},
                   options::OptimizationOptions)
     if !options.autodiff
         error("No gradient or Hessian was provided. Either provide a gradient and Hessian, set autodiff = true in the OptimizationOptions if applicable, or choose a solver that doesn't require a Hessian.")
@@ -172,7 +172,7 @@ end
 function optimize(f::Function,
                   g!::Function,
                   initial_x::Array,
-                  method::Newton,
+                  method::Union{Newton, NewtonTrustRegion},
                   options::OptimizationOptions)
     if !options.autodiff
         error("No Hessian was provided. Either provide a Hessian, set autodiff = true in the OptimizationOptions if applicable, or choose a solver that doesn't require a Hessian.")
@@ -190,7 +190,7 @@ end
 
 function optimize(d::DifferentiableFunction,
                   initial_x::Array,
-                  method::Newton,
+                  method::Union{Newton, NewtonTrustRegion},
                   options::OptimizationOptions)
     if !options.autodiff
         error("No Hessian was provided. Either provide a Hessian, set autodiff = true in the OptimizationOptions if applicable, or choose a solver that doesn't require a Hessian.")

diff --git a/test/newton_trust_region.jl b/test/newton_trust_region.jl
@@ -140,11 +140,14 @@ let
     # Test Optim.newton for all twice differentiable functions in
     # Optim.UnconstrainedProblems.examples
     for (name, prob) in Optim.UnconstrainedProblems.examples
-        if prob.istwicedifferentiable
-            ddf = TwiceDifferentiableFunction(prob.f, prob.g!,prob.h!)
-            res = Optim.optimize(ddf, prob.initial_x, method=NewtonTrustRegion())
-            @assert norm(res.minimum - prob.solutions) < 1e-2
-            @assert res.f_converged || res.x_converged || res.g_converged
-        end
+    	if prob.istwicedifferentiable
+    		ddf = DifferentiableFunction(prob.f, prob.g!)
+    		res = Optim.optimize(ddf, prob.initial_x, NewtonTrustRegion(), OptimizationOptions(autodiff = true))
+    		@assert norm(Optim.minimizer(res) - prob.solutions) < 1e-2
+    		res = Optim.optimize(ddf.f, prob.initial_x, NewtonTrustRegion(), OptimizationOptions(autodiff = true))
+    		@assert norm(Optim.minimizer(res) - prob.solutions) < 1e-2
+            res = Optim.optimize(ddf.f, ddf.g!, prob.initial_x, NewtonTrustRegion(), OptimizationOptions(autodiff = true))
+    		@assert norm(Optim.minimizer(res) - prob.solutions) < 1e-2
+    	end
     end
 end