test deps

Author: Vaibhavdixit02

ext/OptimizationSymbolicAnalysisExt.jl

@@ -7,7 +7,7 @@ import SymbolicAnalysis.Symbolics: variable, Equation, Inequality, unwrap, @vari
 function OptimizationBase.symify_cache(
         f::OptimizationFunction{iip, AD, F, G, FG, H, FGH, HV, C, CJ, CJV, CVJ, CH, HP,
             CJP, CHP, O, EX, CEX, SYS, LH, LHP, HCV, CJCV, CHCV, LHCV},
-        prob, manifold) where {iip, AD, F, G, FG, H, FGH, HV, C, CJ, CJV, CVJ, CH, HP, CJP, CHP, O,
+        prob, num_cons, manifold) where {iip, AD, F, G, FG, H, FGH, HV, C, CJ, CJV, CVJ, CH, HP, CJP, CHP, O,
         EX <: Nothing, CEX <: Nothing, SYS, LH, LHP, HCV, CJCV, CHCV, LHCV}
     obj_expr = f.expr
     cons_expr = f.cons_expr === nothing ? nothing : getfield.(f.cons_expr, Ref(:lhs))

src/cache.jl

@@ -50,7 +50,7 @@ function OptimizationCache(prob::SciMLBase.OptimizationProblem, opt;
         cons_vjp = SciMLBase.allowsconsjvp(opt), cons_jvp = SciMLBase.allowsconsjvp(opt), lag_h = SciMLBase.requireslagh(opt))
 
     if structural_analysis
-        obj_res, cons_res = symify_cache(f, prob, manifold)
+        obj_res, cons_res = symify_cache(f, prob, num_cons, manifold)
     else
         obj_res = nothing
         cons_res = nothing

src/symify.jl

@@ -1,3 +1,3 @@
-function symify_cache(f::OptimizationFunction, prob, manifold)
+function symify_cache(f::OptimizationFunction, prob, num_cons, manifold)
     throw("Structural analysis requires SymbolicAnalysis.jl to be loaded, either add `using SymbolicAnalysis` to your script or set `structural_analysis = false`.")
 end

test/Project.toml

@@ -9,6 +9,7 @@ FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 IterTools = "c8e1da08-722c-5040-9ed9-7db0dc04731e"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 Manifolds = "1cead3c2-87b3-11e9-0ccd-23c62b72b94e"
@@ -22,6 +23,8 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+SparseConnectivityTracer = "9f842d2f-2579-4b1d-911e-f412cf18a3f5"
+SymbolicAnalysis = "4297ee4d-0239-47d8-ba5d-195ecdf594fe"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
@@ -39,4 +42,6 @@ Optim = ">= 1.4.1"
 Optimisers = ">= 0.2.5"
 Optimization = "4"
 OptimizationManopt = "0.0.4"
+SparseConnectivityTracer = "0.6"
+SymbolicAnalysis = "0.3.0"
 SafeTestsets = ">= 0.0.1"

test/cvxtest.jl

@@ -29,7 +29,7 @@ optf = OptimizationFunction(rosenbrock, AutoZygote(), cons = con2_c)
 prob = OptimizationProblem(optf, x0, lcons = [1.0, -Inf], ucons = [1.0, 0.0],
     lb = [-1.0, -1.0], ub = [1.0, 1.0], structural_analysis = true)
 @time res = solve(prob, Optimization.LBFGS(), maxiters = 100)
-@test res.cache.analysis_results.objective.curvature == SymbolicAnalysis.Convex
+@test res.cache.analysis_results.objective.curvature == SymbolicAnalysis.UnknownCurvature
 @test res.cache.analysis_results.constraints[1].curvature == SymbolicAnalysis.Convex
 @test res.cache.analysis_results.constraints[2].curvature ==
       SymbolicAnalysis.UnknownCurvature
@@ -46,7 +46,7 @@ optf = OptimizationFunction(f, Optimization.AutoForwardDiff())
 prob = OptimizationProblem(optf, data2[1]; manifold = M, structural_analysis = true)
 
 opt = OptimizationManopt.GradientDescentOptimizer()
-@time sol = solve(prob, Optimization.LBFGS(), maxiters = 100)
-@test sol.minimizer < 1e-1
+@time sol = solve(prob, opt, maxiters = 100)
+@test sol.minimum < 1e-1
 @test sol.cache.analysis_results.objective.curvature == SymbolicAnalysis.UnknownCurvature
 @test sol.cache.analysis_results.objective.gcurvature == SymbolicAnalysis.GConvex

test/matrixvalued.jl

@@ -78,7 +78,7 @@ using Test, ReverseDiff
         Omega_mc = rand(4, 4) .> 0.5  # Mask for observed entries (boolean matrix)
         X_mc = rand(4, 4)  # Matrix to be completed
         optf = OptimizationFunction{false}(
-            matrix_completion_objective, adtype, cons = rank_constraint)
+            matrix_completion_objective, adtype)
         optf = OptimizationBase.instantiate_function(
             optf, X_mc, adtype, (A_mc, Omega_mc), g = true, h = true)
         optf.grad(X_mc)