Add and update test(s)

Author: Vaibhavdixit02

lib/OptimizationOptimJL/test/runtests.jl

@@ -93,7 +93,7 @@ using Test
     sol = solve(prob, BFGS())
     @test 10 * sol.minimum < l1
 
-    function g!(G, x)
+    function g!(G, x, p = nothing)
         G[1] = -2.0 * (1.0 - x[1]) - 400.0 * (x[2] - x[1]^2) * x[1]
         G[2] = 200.0 * (x[2] - x[1]^2)
     end

test/ADtests.jl

@@ -95,7 +95,7 @@ optprob.grad(G2, x0)
 optprob.hess(H2, x0)
 @test H1 == H2
 
-prob = OptimizationProblem(optprob, x0)
+prob = OptimizationProblem(optf, x0)
 
 sol = solve(prob, Optim.BFGS())
 @test 10 * sol.minimum < l1
@@ -113,7 +113,7 @@ optprob.grad(G2, x0)
 optprob.hess(H2, x0)
 @test H1 == H2
 
-prob = OptimizationProblem(optprob, x0)
+prob = OptimizationProblem(optf, x0)
 
 sol = solve(prob, Optim.BFGS())
 @test 10 * sol.minimum < l1
@@ -132,7 +132,7 @@ optprob.grad(G2, x0)
 optprob.hess(H2, x0)
 @test H1 == H2
 
-prob = OptimizationProblem(optprob, x0)
+prob = OptimizationProblem(optf, x0)
 sol = solve(prob, Optim.BFGS())
 @test 10 * sol.minimum < l1
 
@@ -148,7 +148,8 @@ optprob.grad(G2, x0)
 @test G1 == G2
 @test_throws ErrorException optprob.hess(H2, x0)
 
-prob = OptimizationProblem(optprob, x0)
+
+prob = OptimizationProblem(optf, x0)
 
 sol = solve(prob, Optim.BFGS())
 @test 10 * sol.minimum < l1
@@ -163,7 +164,7 @@ optprob.grad(G2, x0)
 optprob.hess(H2, x0)
 @test H1≈H2 rtol=1e-6
 
-prob = OptimizationProblem(optprob, x0)
+prob = OptimizationProblem(optf, x0)
 sol = solve(prob, Optim.BFGS())
 @test 10 * sol.minimum < l1
 
@@ -248,15 +249,34 @@ H2 = Array{Float64}(undef, 2, 2)
 optprob.hess(H2, [5.0, 3.0])
 @test all(isapprox(H2, [28802.0 -2000.0; -2000.0 200.0]; rtol = 1e-3))
 
-cons_j = optprob.cons_j
-optf = OptimizationFunction(rosenbrock, Optimization.AutoForwardDiff(), cons = con2_c,
-                            cons_j = cons_j, cons_jac_prototype = cons_jac_proto)
-optprob = Optimization.instantiate_function(optf, x0, Optimization.AutoForwardDiff(),
-                                            nothing, 2)
-@test optprob.cons_jac_prototype == sparse([1.0 1.0; 1.0 1.0]) # make sure it's still using it
-J = Array{Float64}(undef, 2, 2)
-optprob.cons_j(J, [5.0, 3.0])
-@test all(isapprox(J, [10.0 6.0; -0.149013 -0.958924]; rtol = 1e-3))
+cons_j = (J, θ, p) -> optprob.cons_j(J, θ)
+hess = (H, θ, p) -> optprob.hess(H, θ)
+sH = sparse([1, 1, 2, 2], [1, 2, 1, 2], zeros(4))
+sJ = sparse([1, 1, 2, 2], [1, 2, 1, 2], zeros(4))
+optf = OptimizationFunction(rosenbrock, Optimization.AutoForwardDiff(), hess=hess, hess_prototype=copy(sH), cons=con2_c, cons_j=cons_j, cons_jac_prototype=copy(sJ))
+optprob1 = Optimization.instantiate_function(optf, x0, Optimization.AutoForwardDiff(), nothing, 2)
+@test optprob1.hess_prototype == sparse([0.0 0.0; 0.0 0.0]) # make sure it's still using it
+optprob1.hess(sH, [5.0, 3.0])
+@test all(isapprox(sH, [28802.0 -2000.0; -2000.0 200.0]; rtol=1e-3))
+@test optprob1.cons_jac_prototype == sparse([0.0 0.0; 0.0 0.0]) # make sure it's still using it
+optprob1.cons_j(sJ, [5.0, 3.0])
+@test all(isapprox(sJ, [10.0 6.0; -0.149013 -0.958924]; rtol=1e-3))
+
+grad = (G, θ, p) -> optprob.grad(G, θ)
+hess = (H, θ, p) -> optprob.hess(H, θ)
+cons_j = (J, θ, p) -> optprob.cons_j(J, θ)
+cons_h = (res, θ, p) -> optprob.cons_h(res, θ)
+sH = sparse([1, 1, 2, 2], [1, 2, 1, 2], zeros(4))
+sJ = sparse([1, 1, 2, 2], [1, 2, 1, 2], zeros(4))
+sH3 = [sparse([1, 2], [1, 2], zeros(2)), sparse([1, 1, 2], [1, 2, 1], zeros(3))]
+optf = OptimizationFunction(rosenbrock, SciMLBase.NoAD(), grad=grad, hess=hess, cons=con2_c, cons_j=cons_j, cons_h=cons_h, hess_prototype=sH, cons_jac_prototype=sJ, cons_hess_prototype=sH3)
+optprob2 = Optimization.instantiate_function(optf, x0, SciMLBase.NoAD(), nothing, 2)
+optprob2.hess(sH, [5.0, 3.0])
+@test all(isapprox(sH, [28802.0 -2000.0; -2000.0 200.0]; rtol=1e-3))
+optprob2.cons_j(sJ, [5.0, 3.0])
+@test all(isapprox(sJ, [10.0 6.0; -0.149013 -0.958924]; rtol=1e-3))
+optprob2.cons_h(sH3, [5.0, 3.0])
+@test sH3 ≈ [[2.0 0.0; 0.0 2.0], [2.8767727327346804 0.2836621681849162; 0.2836621681849162 -6.622738308376736e-9]]
 
 # Can we solve problems? Using AutoForwardDiff to test since we know that works
 for consf in [cons, con2_c]