Remove num_cons from OptimizationFunction

Author: Vaibhavdixit02

src/function.jl

@@ -22,7 +22,7 @@ function default_chunk_size(len)
     end
 end
 
-function instantiate_function(f, x, ::AbstractADType, p)
+function instantiate_function(f, x, ::AbstractADType, p, num_cons = 0)
     grad   = f.grad   === nothing ? nothing : (G,x)->f.grad(G,x,p)
     hess   = f.hess   === nothing ? nothing : (H,x)->f.hess(H,x,p)
     hv     = f.hv     === nothing ? nothing : (H,x,v)->f.hv(H,x,v,p)
@@ -34,10 +34,10 @@ function instantiate_function(f, x, ::AbstractADType, p)
                          typeof(hess),typeof(hv),typeof(cons),
                          typeof(cons_j),typeof(cons_h)}(f.f,
                          DiffEqBase.NoAD(),grad,hess,hv,cons,
-                         cons_j,cons_h,f.num_cons)
+                         cons_j,cons_h)
 end
 
-function instantiate_function(f, x, ::AutoForwardDiff{_chunksize}, p) where _chunksize
+function instantiate_function(f, x, ::AutoForwardDiff{_chunksize}, p, num_cons = 0) where _chunksize
 
     chunksize = _chunksize === nothing ? default_chunk_size(length(x)) : _chunksize
 
@@ -86,7 +86,7 @@ function instantiate_function(f, x, ::AutoForwardDiff{_chunksize}, p) where _chu
 
     if cons !== nothing && f.cons_h === nothing
         cons_h = function (res, θ)
-            for i in 1:f.num_cons
+            for i in 1:num_cons
                 hess_config_cache = ForwardDiff.HessianConfig(x -> cons(x)[i], θ,ForwardDiff.Chunk{chunksize}())
                 ForwardDiff.hessian!(res[i], (x) -> cons(x)[i], θ, hess_config_cache,Val{false}())
             end
@@ -95,11 +95,11 @@ function instantiate_function(f, x, ::AutoForwardDiff{_chunksize}, p) where _chu
         cons_h = f.cons_h
     end
 
-    return OptimizationFunction{true,AutoForwardDiff,typeof(f.f),typeof(grad),typeof(hess),typeof(hv),typeof(cons),typeof(cons_j),typeof(cons_h)}(f.f,AutoForwardDiff(),grad,hess,hv,cons,cons_j,cons_h,f.num_cons)
+    return OptimizationFunction{true,AutoForwardDiff,typeof(f.f),typeof(grad),typeof(hess),typeof(hv),typeof(cons),typeof(cons_j),typeof(cons_h)}(f.f,AutoForwardDiff(),grad,hess,hv,cons,cons_j,cons_h)
 end
 
-function instantiate_function(f, x, ::AutoZygote, p)
-    f.num_cons != 0 && error("AutoZygote does not currently support constraints")
+function instantiate_function(f, x, ::AutoZygote, p, num_cons = 0)
+    num_cons != 0 && error("AutoZygote does not currently support constraints")
 
     _f = θ -> f(θ,p)[1]
     if f.grad === nothing
@@ -135,11 +135,11 @@ function instantiate_function(f, x, ::AutoZygote, p)
         hv = f.hv
     end
 
-    return OptimizationFunction{false,AutoZygote,typeof(f),typeof(grad),typeof(hess),typeof(hv),Nothing,Nothing,Nothing}(f,AutoZygote(),grad,hess,hv,nothing,nothing,nothing,0)
+    return OptimizationFunction{false,AutoZygote,typeof(f),typeof(grad),typeof(hess),typeof(hv),Nothing,Nothing,Nothing}(f,AutoZygote(),grad,hess,hv,nothing,nothing,nothing)
 end
 
-function instantiate_function(f, x, ::AutoReverseDiff, p=DiffEqBase.NullParameters())
-    f.num_cons != 0 && error("AutoReverseDiff does not currently support constraints")
+function instantiate_function(f, x, ::AutoReverseDiff, p=DiffEqBase.NullParameters(), num_cons = 0)
+    num_cons != 0 && error("AutoReverseDiff does not currently support constraints")
 
     _f = θ -> f.f(θ,p)[1]
 
@@ -177,12 +177,12 @@ function instantiate_function(f, x, ::AutoReverseDiff, p=DiffEqBase.NullParamete
         hv = f.hv
     end
 
-    return OptimizationFunction{false,AutoReverseDiff,typeof(f),typeof(grad),typeof(hess),typeof(hv),Nothing,Nothing,Nothing}(f,AutoReverseDiff(),grad,hess,hv,nothing,nothing,nothing,0)
+    return OptimizationFunction{false,AutoReverseDiff,typeof(f),typeof(grad),typeof(hess),typeof(hv),Nothing,Nothing,Nothing}(f,AutoReverseDiff(),grad,hess,hv,nothing,nothing,nothing)
 end
 
 
-function instantiate_function(f, x, ::AutoTracker, p)
-    f.num_cons != 0 && error("AutoTracker does not currently support constraints")
+function instantiate_function(f, x, ::AutoTracker, p, num_cons = 0)
+    num_cons != 0 && error("AutoTracker does not currently support constraints")
     _f = θ -> f.f(θ,p)[1]
 
     if f.grad === nothing
@@ -204,12 +204,11 @@ function instantiate_function(f, x, ::AutoTracker, p)
     end
 
 
-    return OptimizationFunction{false,AutoTracker,typeof(f),typeof(grad),typeof(hess),typeof(hv),Nothing,Nothing,Nothing}(f,AutoTracker(),grad,hess,hv,nothing,nothing,nothing,0)
+    return OptimizationFunction{false,AutoTracker,typeof(f),typeof(grad),typeof(hess),typeof(hv),Nothing,Nothing,Nothing}(f,AutoTracker(),grad,hess,hv,nothing,nothing,nothing)
 end
 
-function instantiate_function(f, x, adtype::AutoFiniteDiff, p)
-
-    f.num_cons != 0 && error("AutoFiniteDiff does not currently support constraints")
+function instantiate_function(f, x, adtype::AutoFiniteDiff, p, num_cons = 0)
+    num_cons != 0 && error("AutoFiniteDiff does not currently support constraints")
     _f = θ -> f.f(θ,p)[1]
 
     if f.grad === nothing
@@ -234,5 +233,5 @@ function instantiate_function(f, x, adtype::AutoFiniteDiff, p)
         hv = f.hv
     end
 
-    return OptimizationFunction{false,AutoFiniteDiff,typeof(f),typeof(grad),typeof(hess),typeof(hv),Nothing,Nothing,Nothing}(f,adtype,grad,hess,hv,nothing,nothing,nothing,0)
+    return OptimizationFunction{false,AutoFiniteDiff,typeof(f),typeof(grad),typeof(hess),typeof(hv),Nothing,Nothing,Nothing}(f,adtype,grad,hess,hv,nothing,nothing,nothing)
 end

src/solve.jl

@@ -215,7 +215,7 @@ function __solve(prob::OptimizationProblem, opt::Optim.ConstrainedOptimizer;cb =
 	  cb_call
 	end
 
-	f = instantiate_function(prob.f,prob.u0,prob.f.adtype,prob.p)
+	f = instantiate_function(prob.f,prob.u0,prob.f.adtype,prob.p,prob.ucons === nothing ? 0 : length(prob.ucons))
 
 	f.cons_j ===nothing && error("Use OptimizationFunction to pass the derivatives or automatically generate them with one of the autodiff backends")
 

test/rosenbrock.jl

@@ -26,7 +26,7 @@ sol = solve(prob, NelderMead())
 @test 10*sol.minimum < l1
 
 cons= (x,p) -> [x[1]^2 + x[2]^2]
-optprob = OptimizationFunction(rosenbrock, GalacticOptim.AutoForwardDiff();cons= cons, num_cons = 1)
+optprob = OptimizationFunction(rosenbrock, GalacticOptim.AutoForwardDiff();cons= cons)
 
 prob = OptimizationProblem(optprob, x0)
 sol = solve(prob, BFGS())
@@ -54,7 +54,7 @@ function con2_c(x,p)
     [x[1]^2 + x[2]^2, x[2]*sin(x[1])-x[1]]
 end
 
-optprob = OptimizationFunction(rosenbrock, GalacticOptim.AutoForwardDiff();cons= con2_c, num_cons = 2)
+optprob = OptimizationFunction(rosenbrock, GalacticOptim.AutoForwardDiff();cons= con2_c)
 prob = OptimizationProblem(optprob, x0, lcons = [-Inf,-Inf], ucons = [Inf,Inf])
 sol = solve(prob, IPNewton())
 @test 10*sol.minimum < l1