Wrap FastLM.jl

Project.toml

@@ -25,9 +25,11 @@ StaticArraysCore = "1e83bf80-4336-4d27-bf5d-d5a4f845583c"
 UnPack = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
 
 [weakdeps]
+FastLevenbergMarquardt = "7a0df574-e128-4d35-8cbd-3d84502bf7ce"
 LeastSquaresOptim = "0fc2ff8b-aaa3-5acd-a817-1944a5e08891"
 
 [extensions]
+NonlinearSolveFastLevenbergMarquardtExt = "FastLevenbergMarquardt"
 NonlinearSolveLeastSquaresOptimExt = "LeastSquaresOptim"
 
 [compat]
@@ -37,6 +39,7 @@ ConcreteStructs = "0.2"
 DiffEqBase = "6.130"
 EnumX = "1"
 Enzyme = "0.11"
+FastLevenbergMarquardt = "0.1"
 FiniteDiff = "2"
 ForwardDiff = "0.10.3"
 LeastSquaresOptim = "0.8"
@@ -57,6 +60,7 @@ julia = "1.9"
 [extras]
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
+FastLevenbergMarquardt = "7a0df574-e128-4d35-8cbd-3d84502bf7ce"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 LeastSquaresOptim = "0fc2ff8b-aaa3-5acd-a817-1944a5e08891"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
@@ -72,4 +76,4 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["Enzyme", "BenchmarkTools", "SafeTestsets", "Pkg", "Test", "ForwardDiff", "StaticArrays", "Symbolics", "LinearSolve", "Random", "LinearAlgebra", "Zygote", "SparseDiffTools", "NonlinearProblemLibrary", "LeastSquaresOptim"]
+test = ["Enzyme", "BenchmarkTools", "SafeTestsets", "Pkg", "Test", "ForwardDiff", "StaticArrays", "Symbolics", "LinearSolve", "Random", "LinearAlgebra", "Zygote", "SparseDiffTools", "NonlinearProblemLibrary", "LeastSquaresOptim", "FastLevenbergMarquardt"]

ext/NonlinearSolveFastLevenbergMarquardtExt.jl

@@ -0,0 +1,71 @@
+module NonlinearSolveFastLevenbergMarquardtExt
+
+using ArrayInterface, NonlinearSolve, SciMLBase
+import ConcreteStructs: @concrete
+import FastLevenbergMarquardt as FastLM
+
+NonlinearSolve.extension_loaded(::Val{:FastLevenbergMarquardt}) = true
+
+function _fast_lm_solver(::FastLevenbergMarquardtSolver{linsolve}, x) where {linsolve}
+    if linsolve == :cholesky
+        return FastLM.CholeskySolver(ArrayInterface.undefmatrix(x))
+    elseif linsolve == :qr
+        return FastLM.QRSolver(eltype(x), length(x))
+    else
+        throw(ArgumentError("Unknown FastLevenbergMarquardt Linear Solver: $linsolve"))
+    end
+end
+
+@concrete struct FastLMCache
+    f!
+    J!
+    prob
+    alg
+    lmworkspace
+    solver
+    kwargs
+end
+
+@concrete struct InplaceFunction{iip} <: Function
+    f
+end
+
+(f::InplaceFunction{true})(fx, x, p) = f.f(fx, x, p)
+(f::InplaceFunction{false})(fx, x, p) = (fx .= f.f(x, p))
+
+function SciMLBase.__init(prob::NonlinearLeastSquaresProblem,
+    alg::FastLevenbergMarquardtSolver, args...; abstol = 1e-8, reltol = 1e-8,
+    verbose = false, maxiters = 1000, kwargs...)
+    iip = SciMLBase.isinplace(prob)
+
+    @assert prob.f.jac!==nothing "FastLevenbergMarquardt requires a Jacobian!"
+
+    f! = InplaceFunction{iip}(prob.f)
+    J! = InplaceFunction{iip}(prob.f.jac)
+
+    resid_prototype = prob.f.resid_prototype === nothing ?
+                      (!iip ? prob.f(prob.u0, prob.p) : zeros(prob.u0)) :
+                      prob.f.resid_prototype
+
+    J = similar(prob.u0, length(resid_prototype), length(prob.u0))
+
+    solver = _fast_lm_solver(alg, prob.u0)
+    LM = FastLM.LMWorkspace(prob.u0, resid_prototype, J)
+
+    return FastLMCache(f!, J!, prob, alg, LM, solver,
+        (; xtol = abstol, ftol = reltol, maxit = maxiters, alg.factor, alg.factoraccept,
+            alg.factorreject, alg.minscale, alg.maxscale, alg.factorupdate, alg.minfactor,
+            alg.maxfactor, kwargs...))
+end
+
+function SciMLBase.solve!(cache::FastLMCache)
+    res, fx, info, iter, nfev, njev, LM, solver = FastLM.lmsolve!(cache.f!, cache.J!,
+        cache.lmworkspace, cache.prob.p; cache.solver, cache.kwargs...)
+    stats = SciMLBase.NLStats(nfev, njev, -1, -1, iter)
+    retcode = info == 1 ? ReturnCode.Success :
+              (info == -1 ? ReturnCode.MaxIters : ReturnCode.Default)
+    return SciMLBase.build_solution(cache.prob, cache.alg, res, fx;
+        retcode, original = (res, fx, info, iter, nfev, njev, LM, solver), stats)
+end
+
+end

ext/NonlinearSolveLeastSquaresOptimExt.jl

@@ -4,7 +4,7 @@ using NonlinearSolve, SciMLBase
 import ConcreteStructs: @concrete
 import LeastSquaresOptim as LSO
 
-extension_loaded(::Val{:LeastSquaresOptim}) = true
+NonlinearSolve.extension_loaded(::Val{:LeastSquaresOptim}) = true
 
 function _lso_solver(::LSOptimSolver{alg, linsolve}) where {alg, linsolve}
     ls = linsolve == :qr ? LSO.QR() :

src/NonlinearSolve.jl

@@ -95,7 +95,8 @@ end
 
 export RadiusUpdateSchemes
 
-export NewtonRaphson, TrustRegion, LevenbergMarquardt, GaussNewton, LSOptimSolver
+export NewtonRaphson, TrustRegion, LevenbergMarquardt, GaussNewton
+export LSOptimSolver, FastLevenbergMarquardtSolver
 
 export LineSearch
 

src/algorithms.jl

@@ -11,18 +11,70 @@ Wrapper over [LeastSquaresOptim.jl](https://github.com/matthieugomez/LeastSquare
 - `linsolve`: Linear solver to use. Can be `:qr`, `:cholesky` or `:lsmr`. If
   `nothing`, then `LeastSquaresOptim.jl` will choose the best linear solver based
   on the Jacobian structure.
+- `autodiff`: Automatic differentiation / Finite Differences. Can be `:central` or `:forward`.
 
 !!! note
     This algorithm is only available if `LeastSquaresOptim.jl` is installed.
 """
 struct LSOptimSolver{alg, linsolve} <: AbstractNonlinearSolveAlgorithm
     autodiff::Symbol
+end
+
+function LSOptimSolver(alg = :lm; linsolve = nothing, autodiff::Symbol = :central)
+    @assert alg in (:lm, :dogleg)
+    @assert linsolve === nothing || linsolve in (:qr, :cholesky, :lsmr)
+    @assert autodiff in (:central, :forward)
+
+    if !extension_loaded(Val(:LeastSquaresOptim))
+        @warn "LeastSquaresOptim.jl is not loaded! It needs to be explicitly loaded \
+               before `solve(prob, LSOptimSolver())` is called."
+    end
+
+    return LSOptimSolver{alg, linsolve}(autodiff)
+end
+
+"""
+    FastLevenbergMarquardtSolver(linsolve = :cholesky)
+
+Wrapper over [FastLevenbergMarquardt.jl](https://github.com/kamesy/FastLevenbergMarquardt.jl) for solving
+`NonlinearLeastSquaresProblem`.
 
-    function LSOptimSolver(alg = :lm; linsolve = nothing, autodiff::Symbol = :central)
-        @assert alg in (:lm, :dogleg)
-        @assert linsolve === nothing || linsolve in (:qr, :cholesky, :lsmr)
-        @assert autodiff in (:central, :forward)
+!!! warning
+    This is not really the fastest solver. It is called that since the original package
+    is called "Fast". `LevenbergMarquardt()` is almost always a better choice.
 
-        return new{alg, linsolve}(autodiff)
+!!! warning
+    This algorithm requires the jacobian function to be provided!
+
+## Arguments:
+
+- `linsolve`: Linear solver to use. Can be `:qr` or `:cholesky`.
+
+!!! note
+    This algorithm is only available if `FastLevenbergMarquardt.jl` is installed.
+"""
+@concrete struct FastLevenbergMarquardtSolver{linsolve} <: AbstractNonlinearSolveAlgorithm
+    factor
+    factoraccept
+    factorreject
+    factorupdate::Symbol
+    minscale
+    maxscale
+    minfactor
+    maxfactor
+end
+
+function FastLevenbergMarquardtSolver(linsolve::Symbol = :cholesky; factor = 1e-6,
+    factoraccept = 13.0, factorreject = 3.0, factorupdate = :marquardt,
+    minscale = 1e-12, maxscale = 1e16, minfactor = 1e-28, maxfactor = 1e32)
+    @assert linsolve in (:qr, :cholesky)
+    @assert factorupdate in (:marquardt, :nielson)
+
+    if !extension_loaded(Val(:FastLevenbergMarquardt))
+        @warn "FastLevenbergMarquardt.jl is not loaded! It needs to be explicitly loaded \
+               before `solve(prob, FastLevenbergMarquardtSolver())` is called."
     end
+
+    return FastLevenbergMarquardtSolver{linsolve}(factor, factoraccept, factorreject,
+        factorupdate, minscale, maxscale, minfactor, maxfactor)
 end

test/nonlinear_least_squares.jl

@@ -1,5 +1,5 @@
-using NonlinearSolve, LinearSolve, LinearAlgebra, Test, Random
-import LeastSquaresOptim
+using NonlinearSolve, LinearSolve, LinearAlgebra, Test, Random, ForwardDiff
+import FastLevenbergMarquardt, LeastSquaresOptim
 
 true_function(x, θ) = @. θ[1] * exp(θ[2] * x) * cos(θ[3] * x + θ[4])
 true_function(y, x, θ) = (@. y = θ[1] * exp(θ[2] * x) * cos(θ[3] * x + θ[4]))
@@ -27,15 +27,26 @@ prob_iip = NonlinearLeastSquaresProblem(NonlinearFunction(loss_function;
         resid_prototype = zero(y_target)), θ_init, x)
 
 nlls_problems = [prob_oop, prob_iip]
-solvers = [
-    GaussNewton(),
-    LevenbergMarquardt(),
-    LSOptimSolver(:lm),
-    LSOptimSolver(:dogleg),
-]
+solvers = [GaussNewton(), LevenbergMarquardt(), LSOptimSolver(:lm), LSOptimSolver(:dogleg)]
 
 for prob in nlls_problems, solver in solvers
     @time sol = solve(prob, solver; maxiters = 10000, abstol = 1e-8)
     @test SciMLBase.successful_retcode(sol)
     @test norm(sol.resid) < 1e-6
 end
+
+function jac!(J, θ, p)
+    ForwardDiff.jacobian!(J, resid -> loss_function(resid, θ, p), θ)
+    return J
+end
+
+prob = NonlinearLeastSquaresProblem(NonlinearFunction(loss_function;
+        resid_prototype = zero(y_target), jac = jac!), θ_init, x)
+
+solvers = [FastLevenbergMarquardtSolver(:cholesky), FastLevenbergMarquardtSolver(:qr)]
+
+for solver in solvers
+    @time sol = solve(prob, solver; maxiters = 10000, abstol = 1e-8)
+    @test SciMLBase.successful_retcode(sol)
+    @test norm(sol.resid) < 1e-6
+end