Add a wrapper for Optimization.jl

Project.toml

@@ -1,7 +1,7 @@
 name = "NonlinearSolve"
 uuid = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
 authors = ["SciML"]
-version = "3.8.3"
+version = "3.9.0"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
@@ -35,8 +35,9 @@ FastLevenbergMarquardt = "7a0df574-e128-4d35-8cbd-3d84502bf7ce"
 FixedPointAcceleration = "817d07cb-a79a-5c30-9a31-890123675176"
 LeastSquaresOptim = "0fc2ff8b-aaa3-5acd-a817-1944a5e08891"
 MINPACK = "4854310b-de5a-5eb6-a2a5-c1dee2bd17f9"
-NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56"
 NLSolvers = "337daf1e-9722-11e9-073e-8b9effe078ba"
+NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56"
+Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 SIAMFANLEquations = "084e46ad-d928-497d-ad5e-07fa361a48c4"
 SpeedMapping = "f1835b91-879b-4a3f-a438-e4baacf14412"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
@@ -48,8 +49,9 @@ NonlinearSolveFastLevenbergMarquardtExt = "FastLevenbergMarquardt"
 NonlinearSolveFixedPointAccelerationExt = "FixedPointAcceleration"
 NonlinearSolveLeastSquaresOptimExt = "LeastSquaresOptim"
 NonlinearSolveMINPACKExt = "MINPACK"
-NonlinearSolveNLsolveExt = "NLsolve"
 NonlinearSolveNLSolversExt = "NLSolvers"
+NonlinearSolveNLsolveExt = "NLsolve"
+NonlinearSolveOptimizationExt = "Optimization"
 NonlinearSolveSIAMFANLEquationsExt = "SIAMFANLEquations"
 NonlinearSolveSpeedMappingExt = "SpeedMapping"
 NonlinearSolveSymbolicsExt = "Symbolics"
@@ -61,8 +63,8 @@ Aqua = "0.8"
 ArrayInterface = "7.7"
 BandedMatrices = "1.4"
 BenchmarkTools = "1.4"
-ConcreteStructs = "0.2.3"
 CUDA = "5.1"
+ConcreteStructs = "0.2.3"
 DiffEqBase = "6.146.0"
 Enzyme = "0.11.15"
 FastBroadcast = "0.2.8"
@@ -71,17 +73,20 @@ FastLevenbergMarquardt = "0.1"
 FiniteDiff = "2.21"
 FixedPointAcceleration = "0.3"
 ForwardDiff = "0.10.36"
+Ipopt = "1.6"
 LazyArrays = "1.8.2"
 LeastSquaresOptim = "0.8.5"
 LineSearches = "7.2"
 LinearAlgebra = "1.10"
 LinearSolve = "2.21"
 MINPACK = "1.2"
 MaybeInplace = "0.1.1"
-NLsolve = "4.5"
 NLSolvers = "0.5"
+NLsolve = "4.5"
 NaNMath = "1"
 NonlinearProblemLibrary = "0.1.2"
+Optimization = "3.24"
+OptimizationMOI = "0.4"
 OrdinaryDiffEq = "6.63"
 Pkg = "1.10"
 PrecompileTools = "1.2"
@@ -93,7 +98,7 @@ RecursiveArrayTools = "3.4"
 Reexport = "1.2"
 SIAMFANLEquations = "1.0.1"
 SafeTestsets = "0.1"
-SciMLBase = "2.19.0"
+SciMLBase = "2.23"
 SimpleNonlinearSolve = "1.2"
 SparseArrays = "1.10"
 SparseDiffTools = "2.14"
@@ -118,14 +123,17 @@ Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
 FastLevenbergMarquardt = "7a0df574-e128-4d35-8cbd-3d84502bf7ce"
 FixedPointAcceleration = "817d07cb-a79a-5c30-9a31-890123675176"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
 LeastSquaresOptim = "0fc2ff8b-aaa3-5acd-a817-1944a5e08891"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
 MINPACK = "4854310b-de5a-5eb6-a2a5-c1dee2bd17f9"
-NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56"
 NLSolvers = "337daf1e-9722-11e9-073e-8b9effe078ba"
+NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56"
 NaNMath = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
 NonlinearProblemLibrary = "b7050fa9-e91f-4b37-bcee-a89a063da141"
+Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
+OptimizationMOI = "fd9f6733-72f4-499f-8506-86b2bdd0dea1"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
@@ -143,4 +151,4 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["Aqua", "Enzyme", "BenchmarkTools", "SafeTestsets", "Pkg", "Test", "ForwardDiff", "StaticArrays", "Symbolics", "LinearSolve", "Random", "LinearAlgebra", "Zygote", "SparseDiffTools", "NonlinearProblemLibrary", "LeastSquaresOptim", "FastLevenbergMarquardt", "NaNMath", "BandedMatrices", "DiffEqBase", "StableRNGs", "MINPACK", "NLsolve", "OrdinaryDiffEq", "SpeedMapping", "FixedPointAcceleration", "SIAMFANLEquations", "Sundials", "ReTestItems", "Reexport", "CUDA", "NLSolvers"]
+test = ["Aqua", "BandedMatrices", "BenchmarkTools", "CUDA", "DiffEqBase", "Enzyme", "FastLevenbergMarquardt", "FixedPointAcceleration", "ForwardDiff", "Ipopt", "LeastSquaresOptim", "LinearAlgebra", "LinearSolve", "MINPACK", "NLSolvers", "NLsolve", "NaNMath", "NonlinearProblemLibrary", "Optimization", "OptimizationMOI", "OrdinaryDiffEq", "Pkg", "Random", "ReTestItems", "Reexport", "SIAMFANLEquations", "SafeTestsets", "SparseDiffTools", "SpeedMapping", "StableRNGs", "StaticArrays", "Sundials", "Symbolics", "Test", "Zygote"]

docs/pages.jl

@@ -17,7 +17,8 @@ pages = ["index.md",
         "native/globalization.md", "native/diagnostics.md"],
     "Wrapped Solver APIs" => Any[
         "api/fastlevenbergmarquardt.md", "api/fixedpointacceleration.md",
-        "api/leastsquaresoptim.md", "api/minpack.md", "api/nlsolve.md", "api/nlsolvers.md",
+        "api/leastsquaresoptim.md", "api/minpack.md",
+        "api/nlsolve.md", "api/nlsolvers.md", "api/optimizationjl.md",
         "api/siamfanlequations.md", "api/speedmapping.md", "api/sundials.md"],
     "Development Documentation" => [
         "devdocs/internal_interfaces.md", "devdocs/linear_solve.md",

docs/src/api/optimizationjl.md

@@ -0,0 +1,17 @@
+# Optimization.jl
+
+This is a extension for importing solvers from Optimization.jl into the SciML Nonlinear
+Problem interface. Note that these solvers do not come by default, and thus one needs to
+install the package before using these solvers:
+
+```julia
+using Pkg
+Pkg.add("Optimization")
+using Optimization, NonlinearSolve
+```
+
+## Solver API
+
+```@docs
+OptimizationJL
+```

docs/src/solvers/nonlinear_least_squares_solvers.md

@@ -29,7 +29,7 @@ fails it falls back to a more robust algorithms ([`LevenbergMarquardt`](@ref),
 ### SimpleNonlinearSolve.jl
 
 These methods are included with NonlinearSolve.jl by default, though SimpleNonlinearSolve.jl
-can be used  directly to reduce dependencies and improve load times.
+can be used directly to reduce dependencies and improve load times.
 SimpleNonlinearSolve.jl's methods excel at small problems and problems defined with static
 arrays.
 
@@ -81,5 +81,8 @@ Submethod choices for this algorithm include:
 
 ### Optimization.jl
 
-`NonlinearLeastSquaresProblem`s can be converted into an `OptimizationProblem`  and used
+`NonlinearLeastSquaresProblem`s can be converted into an `OptimizationProblem` and used
 with any solver of [Optimization.jl](https://github.com/SciML/Optimization.jl).
+
+Alternatively, [`OptimizationJL`](@ref) can be used directly. The only benefit of this is
+that the solver returns [`NonlinearSolution`](@ref) instead of `OptimizationSolution`.

docs/src/solvers/nonlinear_system_solvers.md

@@ -4,7 +4,7 @@
 solve(prob::NonlinearProblem, alg; kwargs...)
 ```
 
-Solves for ``f(u) = 0`` in the problem defined by `prob` using the algorithm `alg`. If no
+Solves for `f(u) = 0` in the problem defined by `prob` using the algorithm `alg`. If no
 algorithm is given, a default algorithm will be chosen.
 
 ## Recommended Methods
@@ -22,7 +22,7 @@ fail to converge. Additionally, [`DynamicSS`](@ref) can be a good choice for hig
 if the root corresponds to a stable equilibrium.
 
 As a balance, [`NewtonRaphson`](@ref) is a good choice for most problems that aren't too
-difficult yet need high performance, and  [`TrustRegion`](@ref) is a bit less performant but
+difficult yet need high performance, and [`TrustRegion`](@ref) is a bit less performant but
 more stable. If the problem is well-conditioned, [`Klement`](@ref) or [`Broyden`](@ref) may
 be faster, but highly dependent on the eigenvalues of the Jacobian being sufficiently small.
 
@@ -177,3 +177,12 @@ This is a wrapper package for importing solvers from NLSolvers.jl into the SciML
     [NLSolvers.jl](https://github.com/JuliaNLSolvers/NLSolvers.jl)
 
 For a list of possible solvers see the [NLSolvers.jl documentation](https://julianlsolvers.github.io/NLSolvers.jl/)
+
+### Optimization.jl
+
+This is a wrapper package for importing solvers from Optimization.jl into the SciML
+Nonlinear Problem interface. These exist mostly for benchmarking purposes and shouldn't
+be used by most users.
+
+  - [`OptimizationJL()`](@ref): A wrapper for
+    [Optimization.jl](https://github.com/SciML/Optimization.jl)

ext/NonlinearSolveOptimizationExt.jl

@@ -0,0 +1,86 @@
+module NonlinearSolveOptimizationExt
+
+using FastClosures, LinearAlgebra, NonlinearSolve, Optimization
+
+function SciMLBase.__solve(
+        prob::NonlinearProblem, alg::OptimizationJL, args...; abstol = nothing,
+        maxiters = 1000, termination_condition = nothing, kwargs...)
+    NonlinearSolve.__test_termination_condition(termination_condition, :OptimizationJL)
+
+    prob.u0 isa Number &&
+        throw(ArgumentError("`OptimizationJL` doesn't support scalar `u0`"))
+
+    _objective_function = if SciMLBase.isinplace(prob)
+        @closure (u, p) -> begin
+            du = similar(u)
+            prob.f(du, u, p)
+            return norm(du, 2)
+        end
+    else
+        @closure (u, p) -> norm(prob.f(u, p), 2)
+    end
+
+    cons = if SciMLBase.isinplace(prob)
+        prob.f
+    else
+        @closure (du, u, p) -> copyto!(du, prob.f(u, p))
+    end
+
+    if alg.autodiff === nothing || alg.autodiff isa SciMLBase.NoAD
+        opt_func = OptimizationFunction(_objective_function; cons)
+    else
+        opt_func = OptimizationFunction(_objective_function, alg.autodiff; cons)
+    end
+    bounds = similar(prob.u0)
+    fill!(bounds, 0)
+    opt_prob = OptimizationProblem(
+        opt_func, prob.u0, prob.p; lcons = bounds, ucons = bounds)
+    sol = solve(opt_prob, alg.solver, args...; abstol, maxiters, kwargs...)
+
+    fu = zero(prob.u0)
+    cons(fu, sol.u, prob.p)
+
+    stats = SciMLBase.NLStats(sol.stats.fevals, sol.stats.gevals, -1, -1, -1)
+
+    return SciMLBase.build_solution(
+        prob, alg, sol.u, fu; retcode = sol.retcode, original = sol, stats)
+end
+
+function SciMLBase.__solve(prob::NonlinearLeastSquaresProblem, alg::OptimizationJL, args...;
+        abstol = nothing, maxiters = 1000, termination_condition = nothing, kwargs...)
+    NonlinearSolve.__test_termination_condition(termination_condition, :OptimizationJL)
+
+    _objective_function = if SciMLBase.isinplace(prob)
+        @closure (θ, p) -> begin
+            resid = prob.f.resid_prototype === nothing ? similar(θ) :
+                    similar(prob.f.resid_prototype, eltype(θ))
+            prob.f(resid, θ, p)
+            return norm(resid, 2)
+        end
+    else
+        @closure (θ, p) -> norm(prob.f(θ, p), 2)
+    end
+
+    if alg.autodiff === nothing || alg.autodiff isa SciMLBase.NoAD
+        opt_func = OptimizationFunction(_objective_function)
+    else
+        opt_func = OptimizationFunction(_objective_function, alg.autodiff)
+    end
+    opt_prob = OptimizationProblem(opt_func, prob.u0, prob.p)
+    sol = solve(opt_prob, alg.solver, args...; abstol, maxiters, kwargs...)
+
+    if SciMLBase.isinplace(prob)
+        resid = prob.f.resid_prototype === nothing ? similar(prob.u0) :
+                prob.f.resid_prototype
+        prob.f(resid, sol.u, prob.p)
+    else
+        resid = prob.f(sol.u, prob.p)
+    end
+
+    stats = SciMLBase.NLStats(sol.stats.fevals, sol.stats.gevals, -1, -1, -1)
+
+    return SciMLBase.build_solution(
+        prob, alg, sol.u, resid; retcode = sol.retcode, original = sol, stats)
+end
+
+end

src/NonlinearSolve.jl

@@ -148,7 +148,7 @@ export NonlinearSolvePolyAlgorithm, RobustMultiNewton, FastShortcutNonlinearPoly
 
 # Extension Algorithms
 export LeastSquaresOptimJL, FastLevenbergMarquardtJL, CMINPACK, NLsolveJL, NLSolversJL,
-       FixedPointAccelerationJL, SpeedMappingJL, SIAMFANLEquationsJL
+       FixedPointAccelerationJL, SpeedMappingJL, SIAMFANLEquationsJL, OptimizationJL
 
 # Advanced Algorithms -- Without Bells and Whistles
 export GeneralizedFirstOrderAlgorithm, ApproximateJacobianSolveAlgorithm, GeneralizedDFSane

src/algorithms/extension_algs.jl

@@ -484,3 +484,40 @@
     end
     return SIAMFANLEquationsJL(method, delta, linsolve, m, beta, autodiff)
 end
+
+"""
+    OptimizationJL(solver, autodiff)
+
+Wrapper over [Optimization.jl](https://docs.sciml.ai/Optimization/stable/) to solve
+Nonlinear Equations and Nonlinear Least Squares Problems.
+
+!!! danger "Using OptimizationJL for Nonlinear Systems"
+
+    This is a absolutely terrible idea. We construct the objective function as the L2-norm
+    of the residual function and impose an equality constraint. This is very inefficient
+    and exists to convince people from HackerNews that this is a horrible idea.
+
+### Arguments
+
+  - `solver`: The solver to use from Optimization.jl. In general for NLLS, all of the
+    solvers will work. However, for nonlinear systems, only the solvers that support
+    equality constraints will work.
+  - `autodiff`: Automatic Differentiation Backend that Optimization.jl should use. See
+    https://docs.sciml.ai/Optimization/stable/API/ad/ for more details. Defaults to
+    `SciMLBase.NoAD()`.
+
+!!! note
+
+    This algorithm is only available if `Optimization.jl` is installed.
+"""
+struct OptimizationJL{S, AD} <: AbstractNonlinearSolveExtensionAlgorithm
+    solver::S
+    autodiff::AD
+
+    function OptimizationJL(solver, autodiff = SciMLBase.NoAD())
+        if Base.get_extension(@__MODULE__, :NonlinearSolveOptimizationExt) === nothing
+            error("OptimizationJL requires Optimization.jl to be loaded")
+        end
+        return new{typeof(solver), typeof(autodiff)}(solver, autodiff)
+    end
+end

test/wrappers/nlls_tests.jl

@@ -46,6 +46,24 @@ end
     end
 end
 
+@testitem "Optimization.jl" setup=[WrapperNLLSSetup] begin
+    import Optimization, OptimizationMOI, Ipopt
+
+    prob_oop = NonlinearLeastSquaresProblem{false}(loss_function, θ_init, x)
+    prob_iip = NonlinearLeastSquaresProblem(
+        NonlinearFunction(loss_function; resid_prototype = zero(y_target)), θ_init, x)
+
+    nlls_problems = [prob_oop, prob_iip]
+
+    solver = OptimizationJL(Ipopt.Optimizer(), AutoForwardDiff())
+
+    for prob in nlls_problems
+        sol = solve(prob, solver; maxiters = 10000, abstol = 1e-8)
+        # Ipopt fails currently
+        @test sol isa SciMLBase.NonlinearSolution
+    end
+end
+
 @testitem "FastLevenbergMarquardt.jl + CMINPACK: Jacobian Provided" setup=[WrapperNLLSSetup] begin
     function jac!(J, θ, p)
         resid = zeros(length(p))

test/wrappers/rootfind_tests.jl

@@ -2,8 +2,9 @@
 using Reexport
 @reexport using LinearAlgebra
 import NLSolvers, NLsolve, SIAMFANLEquations, MINPACK
+import Optimization, OptimizationMOI, Ipopt
 
-export NLSolvers
+export NLSolvers, Ipopt
 end
 
 @testitem "Steady State Problems" setup=[WrapperRootfindImports] begin
@@ -48,7 +49,8 @@ end
 
     for alg in [
         NLSolversJL(NLSolvers.LineSearch(NLSolvers.Newton(), NLSolvers.Backtracking())),
-        NLsolveJL(), CMINPACK(), SIAMFANLEquationsJL()]
+        NLsolveJL(), CMINPACK(), SIAMFANLEquationsJL(),
+        OptimizationJL(Ipopt.Optimizer(), AutoForwardDiff())]
         local sol
         sol = solve(prob_iip, alg)
         @test SciMLBase.successful_retcode(sol.retcode)
@@ -61,7 +63,8 @@ end
     prob_oop = NonlinearProblem{false}(f_oop, u0)
     for alg in [
         NLSolversJL(NLSolvers.LineSearch(NLSolvers.Newton(), NLSolvers.Backtracking())),
-        NLsolveJL(), CMINPACK(), SIAMFANLEquationsJL()]
+        NLsolveJL(), CMINPACK(), SIAMFANLEquationsJL(),
+        OptimizationJL(Ipopt.Optimizer(), AutoForwardDiff())]
         local sol
         sol = solve(prob_oop, alg)
         @test SciMLBase.successful_retcode(sol.retcode)
@@ -128,4 +131,8 @@ end
     @test maximum(abs, sol.resid) < 1e-6
     sol = solve(ProbN, SIAMFANLEquationsJL(; method = :pseudotransient); abstol = 1e-8)
     @test maximum(abs, sol.resid) < 1e-6
+    sol = solve(ProbN, OptimizationJL(Ipopt.Optimizer(), AutoForwardDiff()); abstol = 1e-8)
+    @test maximum(abs, sol.resid) < 1e-6
+    sol = solve(ProbN, OptimizationJL(Ipopt.Optimizer(), AutoFiniteDiff()); abstol = 1e-8)
+    @test maximum(abs, sol.resid) < 1e-6
 end