Fix unhelpful error message for missing constraint bounds

Project.toml

@@ -7,6 +7,7 @@ ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
 ArrayInterface = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
 ConsoleProgressMonitor = "88cd18e8-d9cc-4ea6-8889-5259c0d15c8b"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
+ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 LBFGSB = "5be7bae1-8223-5378-bac3-9e7378a2f6e6"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
@@ -43,6 +44,7 @@ Lux = "1.12.4"
 MLUtils = "0.4"
 ModelingToolkit = "9"
 Optim = ">= 1.4.1"
+Optimisers = ">= 0.2.5"
 OptimizationBase = "2"
 OptimizationMOI = "0.5"
 OptimizationOptimJL = "0.4"
@@ -51,7 +53,7 @@ OrdinaryDiffEqTsit5 = "1"
 Pkg = "1"
 Printf = "1.10"
 ProgressLogging = "0.1"
-Random = "1.10" 
+Random = "1.10"
 Reexport = "1.2"
 ReverseDiff = "1"
 SafeTestsets = "0.1"
@@ -63,7 +65,6 @@ Symbolics = "6"
 TerminalLoggers = "0.1"
 Test = "1.10"
 Tracker = "0.2"
-Optimisers = ">= 0.2.5"
 Zygote = "0.6, 0.7"
 julia = "1.10"
 
@@ -101,7 +102,4 @@ Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["Aqua", "BenchmarkTools", "Boltz", "ComponentArrays", "DiffEqFlux", "Enzyme", "FiniteDiff", "Flux", "ForwardDiff",
-    "Ipopt", "IterTools", "Lux", "MLUtils", "ModelingToolkit", "Optim", "OptimizationMOI", "OptimizationOptimJL", "OptimizationOptimisers", 
-    "OrdinaryDiffEqTsit5", "Pkg", "Random", "ReverseDiff", "SafeTestsets", "SciMLSensitivity", "SparseArrays", "SparseDiffTools",
-    "Symbolics",  "Test", "Tracker", "Zygote"]
+test = ["Aqua", "BenchmarkTools", "Boltz", "ComponentArrays", "DiffEqFlux", "Enzyme", "FiniteDiff", "Flux", "ForwardDiff", "Ipopt", "IterTools", "Lux", "MLUtils", "ModelingToolkit", "Optim", "OptimizationMOI", "OptimizationOptimJL", "OptimizationOptimisers", "OrdinaryDiffEqTsit5", "Pkg", "Random", "ReverseDiff", "SafeTestsets", "SciMLSensitivity", "SparseArrays", "SparseDiffTools", "Symbolics", "Test", "Tracker", "Zygote"]

lib/OptimizationEvolutionary/src/OptimizationEvolutionary.jl

@@ -97,6 +97,11 @@ function SciMLBase.__solve(cache::OptimizationCache{
         P,
         C
 }
+    # Check constraint validation if constraints are present
+    if !isnothing(cache.f.cons)
+        Optimization._check_constrained_problem(cache)
+    end
+
     local x, cur, state
 
     function _cb(trace)

lib/OptimizationMOI/src/nlp.jl

@@ -518,6 +518,14 @@ function _add_moi_variables!(opt_setup, evaluator::MOIOptimizationNLPEvaluator)
 end
 
 function SciMLBase.__solve(cache::MOIOptimizationNLPCache)
+    # Check constraint validation for MOI NLP optimizer
+    if !isnothing(cache.evaluator.f.cons)
+        if isnothing(cache.evaluator.lcons) || isnothing(cache.evaluator.ucons)
+            throw(ArgumentError("Constrained optimization problem requires both `lcons` and `ucons` to be provided to OptimizationProblem. " *
+                                "Example: OptimizationProblem(optf, u0, p; lcons=[-Inf], ucons=[0.0])"))
+        end
+    end
+
     maxiters = Optimization._check_and_convert_maxiters(cache.solver_args.maxiters)
     maxtime = Optimization._check_and_convert_maxtime(cache.solver_args.maxtime)
     opt_setup = __map_optimizer_args(cache,

lib/OptimizationNLopt/src/OptimizationNLopt.jl

@@ -152,6 +152,11 @@ function SciMLBase.__solve(cache::OptimizationCache{
         P,
         C
 }
+    # Check constraint validation if this solver supports constraints
+    if SciMLBase.allowsconstraints(cache.opt)
+        Optimization._check_constrained_problem(cache)
+    end
+
     local x
 
     _loss = function (θ)

lib/OptimizationNOMAD/src/OptimizationNOMAD.jl

@@ -52,6 +52,14 @@ function SciMLBase.__solve(prob::OptimizationProblem, opt::NOMADOpt;
         reltol::Union{Number, Nothing} = nothing,
         cons_method = ExtremeBarrierMethod,
         kwargs...)
+    # Check constraint validation for NOMAD
+    if !isnothing(prob.f.cons)
+        if isnothing(prob.lcons) || isnothing(prob.ucons)
+            throw(ArgumentError("Constrained optimization problem requires both `lcons` and `ucons` to be provided to OptimizationProblem. " *
+                                "Example: OptimizationProblem(optf, u0, p; lcons=[-Inf], ucons=[0.0])"))
+        end
+    end
+
     local x
 
     maxiters = Optimization._check_and_convert_maxiters(maxiters)

lib/OptimizationOptimJL/src/OptimizationOptimJL.jl

@@ -344,6 +344,9 @@ function SciMLBase.__solve(cache::OptimizationCache{
         D,
         P
 }
+    # Check constraint validation for constrained optimizers
+    Optimization._check_constrained_problem(cache)
+
     local x, cur, state
 
     function _cb(trace)

lib/OptimizationPRIMA/src/OptimizationPRIMA.jl

@@ -129,6 +129,11 @@ function SciMLBase.__solve(cache::Optimization.OptimizationCache{
         P,
         C
 }
+    # Check constraint validation if this solver supports constraints
+    if SciMLBase.allowsconstraints(cache.opt)
+        Optimization._check_constrained_problem(cache)
+    end
+
     iter = 0
     _loss = function (θ)
         x = cache.f(θ, cache.p)

lib/OptimizationSciPy/src/OptimizationSciPy.jl

@@ -308,6 +308,11 @@ end
 
 function SciMLBase.__solve(cache::OptimizationCache{F,RC,LB,UB,LC,UC,S,O,D,P,C}) where 
                           {F,RC,LB,UB,LC,UC,S,O<:ScipyMinimize,D,P,C}
+    # Check constraint validation if this solver supports constraints
+    if SciMLBase.allowsconstraints(cache.opt)
+        Optimization._check_constrained_problem(cache)
+    end
+
     local cons_cache = nothing
     if !isnothing(cache.f.cons) && !isnothing(cache.lcons)
         cons_cache = zeros(eltype(cache.u0), length(cache.lcons))
@@ -1193,6 +1198,9 @@ end
 
 function SciMLBase.__solve(cache::OptimizationCache{F,RC,LB,UB,LC,UC,S,O,D,P,C}) where 
                           {F,RC,LB,UB,LC,UC,S,O<:ScipyShgo,D,P,C}
+    # Check constraint validation for ScipyShgo
+    Optimization._check_constrained_problem(cache)
+
     local cons_cache = nothing
     if !isnothing(cache.f.cons) && !isnothing(cache.lcons)
         cons_cache = zeros(eltype(cache.u0), length(cache.lcons))

src/auglag.jl

@@ -80,6 +80,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
     solver_kwargs = __map_optimizer_args(cache, cache.opt; maxiters, cache.solver_args...)
 
     if !isnothing(cache.f.cons)
+        _check_constrained_problem(cache)
         eq_inds = [cache.lcons[i] == cache.ucons[i] for i in eachindex(cache.lcons)]
         ineq_inds = (!).(eq_inds)
 

src/lbfgsb.jl

@@ -98,6 +98,7 @@ function SciMLBase.__solve(cache::OptimizationCache{
     solver_kwargs = __map_optimizer_args(cache, cache.opt; maxiters, cache.solver_args...)
 
     if !isnothing(cache.f.cons)
+        _check_constrained_problem(cache)
         eq_inds = [cache.lcons[i] == cache.ucons[i] for i in eachindex(cache.lcons)]
         ineq_inds = (!).(eq_inds)
 
@@ -124,7 +125,8 @@ function SciMLBase.__solve(cache::OptimizationCache{
             cache.f.cons(cons_tmp, θ)
             cons_tmp[eq_inds] .= cons_tmp[eq_inds] - cache.lcons[eq_inds]
             cons_tmp[ineq_inds] .= cons_tmp[ineq_inds] .- cache.ucons[ineq_inds]
-            opt_state = Optimization.OptimizationState(u = θ, objective = x[1], p = cache.p, iter = iter_count[])
+            opt_state = Optimization.OptimizationState(
+                u = θ, objective = x[1], p = cache.p, iter = iter_count[])
             if cache.callback(opt_state, x...)
                 error("Optimization halted by callback.")
             end
@@ -212,7 +214,8 @@ function SciMLBase.__solve(cache::OptimizationCache{
         _loss = function (θ)
             x = cache.f(θ, cache.p)
             iter_count[] += 1
-            opt_state = Optimization.OptimizationState(u = θ, objective = x[1], p = cache.p, iter = iter_count[])
+            opt_state = Optimization.OptimizationState(
+                u = θ, objective = x[1], p = cache.p, iter = iter_count[])
             if cache.callback(opt_state, x...)
                 error("Optimization halted by callback.")
             end

src/utils.jl

@@ -52,6 +52,19 @@ function _check_and_convert_maxtime(maxtime)
     end
 end
 
+function _check_constrained_problem(cache::OptimizationCache)
+    """
+    Check that if constraints are present, both lcons and ucons are provided.
+    This validation is called by solvers that support constrained optimization.
+    """
+    if !isnothing(cache.f.cons)
+        if isnothing(cache.lcons) || isnothing(cache.ucons)
+            throw(ArgumentError("Constrained optimization problem requires both `lcons` and `ucons` to be provided to OptimizationProblem. " *
+                                "Example: OptimizationProblem(optf, u0, p; lcons=[-Inf], ucons=[0.0])"))
+        end
+    end
+end
+
 # RetCode handling for BBO and others.
 using SciMLBase: ReturnCode
 

test/native.jl

@@ -61,3 +61,52 @@ optf1 = OptimizationFunction(loss, AutoSparseForwardDiff())
 prob1 = OptimizationProblem(optf1, rand(5), data)
 sol1 = solve(prob1, OptimizationOptimisers.Adam(), maxiters = 1000, callback = callback)
 @test sol1.objective < l0
+
+# Test constraint bounds validation (issue #959)
+@testset "Constraint bounds validation" begin
+    # Test case from issue #959 - missing lcons and ucons should give helpful error
+    rosenbrock_constrained(u, p) = (p[1] - u[1])^2 + p[2] * (u[2] - u[1]^2)^2
+
+    function cons_missing_bounds!(out, x, p)
+        out[1] = sum(x)
+    end
+
+    optf_missing = OptimizationFunction(
+        rosenbrock_constrained, AutoForwardDiff(), cons = cons_missing_bounds!)
+    prob_missing = OptimizationProblem(optf_missing, [-1, 1.0], [1.0, 100.0])
+
+    # Test LBFGS
+    @test_throws ArgumentError solve(prob_missing, Optimization.LBFGS())
+
+    # Verify the error message is helpful
+    try
+        solve(prob_missing, Optimization.LBFGS())
+    catch e
+        @test isa(e, ArgumentError)
+        @test occursin("lcons", e.msg)
+        @test occursin("ucons", e.msg)
+        @test occursin("OptimizationProblem", e.msg)
+        @test occursin("Example:", e.msg)
+    end
+
+    # Test AugLag
+    @test_throws ArgumentError solve(prob_missing, Optimization.AugLag())
+
+    # Verify the error message is helpful for AugLag too
+    try
+        solve(prob_missing, Optimization.AugLag())
+    catch e
+        @test isa(e, ArgumentError)
+        @test occursin("lcons", e.msg)
+        @test occursin("ucons", e.msg)
+        @test occursin("OptimizationProblem", e.msg)
+        @test occursin("Example:", e.msg)
+    end
+
+    # Test that it works when lcons and ucons are provided
+    prob_with_bounds = OptimizationProblem(
+        optf_missing, [-1, 1.0], [1.0, 100.0], lcons = [-Inf], ucons = [0.0])
+    # This should not throw an error (though it may not converge)
+    sol = solve(prob_with_bounds, Optimization.LBFGS(), maxiters = 10)
+    @test !isnothing(sol)
+end