Repair downgrade sublibrary follow-ups

Co-Authored-By: Chris Rackauckas <accounts@chrisrackauckas.com>

Author: ChrisRackauckas

lib/OptimizationEvolutionary/Project.toml

@@ -8,9 +8,6 @@ Evolutionary = "86b6b26d-c046-49b6-aa0b-5f0f74682bd6"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 
-[weakdeps]
-LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
-
 [extras]
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
@@ -22,7 +19,6 @@ OptimizationBase = {path = "../OptimizationBase"}
 
 [compat]
 Evolutionary = "0.11"
-LogExpFunctions = "0.3.28"
 OptimizationBase = "5"
 Pkg = "1"
 Random = "1.10"

lib/OptimizationMadNLP/Project.toml

@@ -29,7 +29,7 @@ Random = "1.10"
 Reexport = "1.2"
 ReverseDiff = "1"
 SafeTestsets = "0.1"
-SciMLBase = "2.130, 3"
+SciMLBase = "2.146, 3"
 SparseArrays = "1.10"
 SymbolicIndexingInterface = "0.3.46"
 Symbolics = "7"

lib/OptimizationMadNLP/test/core_tests.jl

@@ -364,16 +364,18 @@ end
                 MadNLP.CompactLBFGS,
                 MadNLP.ExactHessian,  # For comparison
             ]
+            initial_u = variant == MadNLP.ExactHessian ? x0[1:2] : x0
             # Only provide gradients, no Hessian needed for LBFGS
             ad = AutoForwardDiff()  # First-order AD is sufficient
             optfunc = OptimizationFunction(extended_rosenbrock, ad)
-            prob = OptimizationProblem(optfunc, x0, nothing)
+            prob = OptimizationProblem(optfunc, initial_u, nothing)
 
             if variant == MadNLP.ExactHessian
+                # Exact Hessian is only a comparison here; the LBFGS path keeps the larger problem.
                 # Use second-order AD for exact Hessian
                 ad = SecondOrder(AutoForwardDiff(), AutoForwardDiff())
                 optfunc = OptimizationFunction(extended_rosenbrock, ad)
-                prob = OptimizationProblem(optfunc, x0, nothing)
+                prob = OptimizationProblem(optfunc, initial_u, nothing)
             end
 
             opt = MadNLPOptimizer(
@@ -458,8 +460,8 @@ end
             return x0
         end
 
-        @testset "N=5 electrons with $approx" for approx in [MadNLP.CompactLBFGS, MadNLP.ExactHessian]
-            np = 5
+        @testset "electrons on sphere with $approx" for approx in [MadNLP.CompactLBFGS, MadNLP.ExactHessian]
+            np = approx == MadNLP.ExactHessian ? 2 : 5
             x0 = init_electrons_on_sphere(np)
 
             if approx == MadNLP.CompactLBFGS
@@ -499,10 +501,9 @@ end
             unit_sphere_constraints(cons_vals, sol.u, nothing)
             @test all(abs.(cons_vals) .< 1.0e-5)
 
-            # Known optimal energy for 5 electrons on unit sphere
+            # Known optimal energy for electrons on unit sphere
             # Reference: https://en.wikipedia.org/wiki/Thomson_problem
-            # Configuration: Triangular dipyramid (trigonal bipyramid, D3h symmetry)
-            expected_energy = 6.474691495
+            expected_energy = np == 2 ? 0.5 : 6.474691495
             @test isapprox(sol.objective, expected_energy, rtol = 1.0e-3)
 
             # Verify minimum distance between electrons
@@ -520,26 +521,26 @@ end
             @test min_dist > 0.5  # Electrons should be well-separated
         end
 
-        @testset verbose = true "LBFGS vs Exact Hessian" begin
-            # Test with moderate size to show LBFGS efficiency
-            np = 10  # Gyroelongated square dipyramid configuration
-            x0 = init_electrons_on_sphere(np)
-
+        @testset verbose = true "LBFGS and Exact Hessian" begin
             results = []
 
-            for (name, approx, ad) in [
+            for (name, approx, ad, np, expected_energy) in [
                     (
                         "CompactLBFGS", MadNLP.CompactLBFGS,
-                        AutoForwardDiff(),
+                        AutoForwardDiff(), 10,
+                        32.71694946,
                     )
                     (
                         "ExactHessian",
                         MadNLP.ExactHessian,
                         SecondOrder(
                             AutoForwardDiff(), AutoZygote()
                         ),
+                        2,
+                        0.5,
                     )
                 ]
+                x0 = init_electrons_on_sphere(np)
                 optfunc = OptimizationFunction(
                     coulomb_potential, ad,
                     cons = unit_sphere_constraints
@@ -562,18 +563,18 @@ end
                         objective = sol.objective,
                         iterations = sol.stats.iterations,
                         success = SciMLBase.successful_retcode(sol),
+                        expected = expected_energy,
                     )
                 )
             end
 
             # All methods should converge
             @test all(r[2].success for r in values(results))
 
-            # All should find similar objective values (gyroelongated square dipyramid energy)
+            # Check objective values against the corresponding Thomson problem energy.
             # Reference: https://en.wikipedia.org/wiki/Thomson_problem
-            objectives = [r[2].objective for r in values(results)]
-            @testset "$(results[i][1])" for (i, o) in enumerate(objectives)
-                @test o ≈ 32.71694946 rtol = 1.0e-2
+            @testset "$(name)" for (name, result) in results
+                @test result.objective ≈ result.expected rtol = 1.0e-2
             end
 
             # LBFGS methods typically need more iterations but less cost per iteration

lib/SimpleOptimization/Project.toml

@@ -40,7 +40,7 @@ Pkg = "1"
 Reexport = "1.2"
 SafeTestsets = "0.1"
 SciMLBase = "2.122.1, 3"
-SimpleNonlinearSolve = "2.5"
+SimpleNonlinearSolve = "2.12"
 Test = "1.10"
 julia = "1.10"
 

lib/SimpleOptimization/src/SimpleOptimization.jl

@@ -187,7 +187,7 @@ function SciMLBase.__solve(cache::OptimizationCache{O}) where {O <: SimpleLBFGS}
         nlprob,
         SimpleLimitedMemoryBroyden(;
             threshold = __get_threshold(cache.opt),
-            linesearch = false
+            linesearch = nothing
         );
         maxiters = maxiters,
         abstol = abstol,
@@ -228,7 +228,7 @@ function SciMLBase.__solve(cache::OptimizationCache{O}) where {O <: SimpleBFGS}
     nlprob = NonlinearProblem(∇f, cache.u0)
     nlsol = solve(
         nlprob,
-        SimpleBroyden(; linesearch = false);
+        SimpleBroyden(; linesearch = nothing);
         maxiters = maxiters,
         abstol = abstol,
         reltol = reltol