mtk doesn't have lagh

Author: Vaibhavdixit02

lib/OptimizationMOI/Project.toml

@@ -41,8 +41,9 @@ Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
 Ipopt_jll = "9cc047cb-c261-5740-88fc-0cf96f7bdcc7"
 Juniper = "2ddba703-00a4-53a7-87a5-e8b9971dde84"
 NLopt = "76087f3c-5699-56af-9a33-bf431cd00edd"
+ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["AmplNLWriter", "HiGHS", "Ipopt", "Ipopt_jll", "Juniper", "NLopt", "Test", "Zygote"]
+test = ["AmplNLWriter", "HiGHS", "Ipopt", "Ipopt_jll", "Juniper", "NLopt", "ReverseDiff", "Test", "Zygote"]

lib/OptimizationMOI/src/nlp.jl

@@ -113,8 +113,16 @@ function MOIOptimizationNLPCache(prob::OptimizationProblem,
     reinit_cache = OptimizationBase.ReInitCache(prob.u0, prob.p) # everything that can be changed via `reinit`
 
     num_cons = prob.ucons === nothing ? 0 : length(prob.ucons)
-    f = Optimization.instantiate_function(prob.f, reinit_cache, prob.f.adtype, num_cons;
-        g = true, h = true, cons_j = true, cons_vjp = true, lag_h = true)
+    if prob.f.adtype isa ADTypes.AutoSymbolics || (prob.f.adtype isa ADTypes.AutoSparse &&
+        prob.f.adtype.dense_ad isa ADTypes.AutoSymbolics)
+        f = Optimization.instantiate_function(
+            prob.f, reinit_cache, prob.f.adtype, num_cons;
+            g = true, h = true, cons_j = true, cons_h = true)
+    else
+        f = Optimization.instantiate_function(
+            prob.f, reinit_cache, prob.f.adtype, num_cons;
+            g = true, h = true, cons_j = true, cons_vjp = true, lag_h = true)
+    end
     T = eltype(prob.u0)
     n = length(prob.u0)
 
@@ -290,7 +298,8 @@ function MOI.eval_constraint_jacobian(evaluator::MOIOptimizationNLPEvaluator, j,
     return
 end
 
-function MOI.eval_constraint_jacobian_product(evaluator::MOIOptimizationNLPEvaluator, y, x, w)
+function MOI.eval_constraint_jacobian_product(
+        evaluator::MOIOptimizationNLPEvaluator, y, x, w)
     if evaluator.f.cons_jvp !== nothing
         evaluator.f.cons_jvp(y, x, w)
 

lib/OptimizationMOI/test/runtests.jl

@@ -1,4 +1,4 @@
-using OptimizationMOI, Optimization, Ipopt, NLopt, Zygote, ModelingToolkit
+using OptimizationMOI, Optimization, Ipopt, NLopt, Zygote, ModelingToolkit, ReverseDiff
 using AmplNLWriter, Ipopt_jll, Juniper, HiGHS
 using Test, SparseArrays
 

lib/OptimizationMultistartOptimization/Project.toml

@@ -16,8 +16,9 @@ Reexport = "1.2"
 
 [extras]
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["ForwardDiff", "Pkg", "Test"]
+test = ["ForwardDiff", "ReverseDiff", "Pkg", "Test"]

lib/OptimizationMultistartOptimization/test/runtests.jl

@@ -1,7 +1,7 @@
 using Pkg;
 Pkg.develop(path = joinpath(@__DIR__, "../../", "OptimizationNLopt"));
 using OptimizationMultistartOptimization, Optimization, ForwardDiff, OptimizationNLopt
-using Test
+using Test, ReverseDiff
 
 @testset "OptimizationMultistartOptimization.jl" begin
     rosenbrock(x, p) = (p[1] - x[1])^2 + p[2] * (x[2] - x[1]^2)^2

test/diffeqfluxtests.jl

@@ -99,12 +99,12 @@ prob = Optimization.OptimizationProblem(optprob, pp)
 result_neuralode = Optimization.solve(prob,
     OptimizationOptimisers.ADAM(), callback = callback,
     maxiters = 300)
-@test result_neuralode.objective ≈ loss_neuralode(result_neuralode.u)[1] rtol = 1e-2
+@test result_neuralode.objective≈loss_neuralode(result_neuralode.u)[1] rtol=1e-2
 
 prob2 = remake(prob, u0 = result_neuralode.u)
 result_neuralode2 = Optimization.solve(prob2,
     BFGS(initial_stepnorm = 0.0001),
     callback = callback,
     maxiters = 100)
-@test result_neuralode2.objective ≈ loss_neuralode(result_neuralode2.u)[1] rtol = 1e-2
+@test result_neuralode2.objective≈loss_neuralode(result_neuralode2.u)[1] rtol=1e-2
 @test result_neuralode2.objective < 10