Add NLsolve

Project.toml

@@ -83,6 +83,7 @@ SparseArrays = "<0.0.1, 1"
 SparseDiffTools = "2.14"
 StableRNGs = "1"
 StaticArrays = "1"
+SteadyStateDiffEq = "2"
 Symbolics = "5"
 Test = "1"
 UnPack = "1.0"

docs/src/api/nlsolve.md

@@ -13,5 +13,5 @@ using NLSolve, NonlinearSolve
 ## Solver API
 
 ```@docs
-NLSolveJL
+NLsolveJL
 ```

docs/src/solvers/NonlinearSystemSolvers.md

@@ -114,7 +114,7 @@ computationally expensive than direct methods.
 
 This is a wrapper package for importing solvers from NLsolve.jl into the SciML interface.
 
-  - `NLSolveJL()`: A wrapper for [NLsolve.jl](https://github.com/JuliaNLSolvers/NLsolve.jl)
+  - `NLsolveJL()`: A wrapper for [NLsolve.jl](https://github.com/JuliaNLSolvers/NLsolve.jl)
 
 Submethod choices for this algorithm include:
 

ext/NonlinearSolveMINPACKExt.jl

@@ -19,7 +19,6 @@ function SciMLBase.__solve(prob::Union{NonlinearProblem{uType, iip},
     # unwrapping alg params
     show_trace = alg.show_trace
     tracing = alg.tracing
-    io = alg.io
 
     if !iip && prob.u0 isa Number
         f! = (du, u) -> (du .= prob.f(first(u), p); Cint(0))
@@ -36,9 +35,10 @@ function SciMLBase.__solve(prob::Union{NonlinearProblem{uType, iip},
     u = zero(u0)
     resid = NonlinearSolve.evaluate_f(prob, u)
     m = length(resid)
+    size_jac = (length(resid), length(u))
 
     method = ifelse(alg.method === :auto,
-        ifelse(prob isa NonlinearLeastSquaresProblem, :lm, :hydr), alg.method)
+        ifelse(prob isa NonlinearLeastSquaresProblem, :lm, :hybr), alg.method)
 
     if SciMLBase.has_jac(prob.f)
         if !iip && prob.u0 isa Number
@@ -51,9 +51,8 @@ function SciMLBase.__solve(prob::Union{NonlinearProblem{uType, iip},
             g! = (du, u) -> prob.f.jac(du, u, p)
         else # Then it's an in-place function on an abstract array
             g! = function (du, u)
-                prob.f.jac(reshape(du, sizeu), reshape(u, sizeu), p)
-                du = vec(du)
-                return CInt(0)
+                prob.f.jac(reshape(du, size_jac), reshape(u, sizeu), p)
+                return Cint(0)
             end
         end
         original = MINPACK.fsolve(f!, g!, u0, m; tol = abstol, show_trace, tracing, method,

ext/NonlinearSolveNLsolveExt.jl

@@ -1,3 +1,80 @@
 module NonlinearSolveNLsolveExt
 
+using NonlinearSolve, NLsolve, DiffEqBase, SciMLBase
+import UnPack: @unpack
+
+function SciMLBase.__solve(prob::NonlinearProblem, alg::NLsolveJL, args...; abstol = 1e-6,
+        maxiters = 1000, alias_u0::Bool = false, kwargs...)
+    if typeof(prob.u0) <: Number
+        u0 = [prob.u0]
+    else
+        u0 = NonlinearSolve.__maybe_unaliased(prob.u0, alias_u0)
+    end
+
+    iip = isinplace(prob)
+
+    sizeu = size(prob.u0)
+    p = prob.p
+
+    # unwrapping alg params
+    @unpack method, autodiff, store_trace, extended_trace, linesearch, linsolve = alg
+    @unpack factor, autoscale, m, beta, show_trace = alg
+
+    if !iip && prob.u0 isa Number
+        f! = (du, u) -> (du .= prob.f(first(u), p); Cint(0))
+    elseif !iip && prob.u0 isa Vector{Float64}
+        f! = (du, u) -> (du .= prob.f(u, p); Cint(0))
+    elseif !iip && prob.u0 isa AbstractArray
+        f! = (du, u) -> (du .= vec(prob.f(reshape(u, sizeu), p)); Cint(0))
+    elseif prob.u0 isa Vector{Float64}
+        f! = (du, u) -> prob.f(du, u, p)
+    else # Then it's an in-place function on an abstract array
+        f! = (du, u) -> (prob.f(reshape(du, sizeu), reshape(u, sizeu), p); du = vec(du); 0)
+    end
+
+    if prob.u0 isa Number
+        resid = [NonlinearSolve.evaluate_f(prob, first(u0))]
+    else
+        resid = NonlinearSolve.evaluate_f(prob, u0)
+    end
+
+    size_jac = (length(resid), length(u0))
+
+    if SciMLBase.has_jac(prob.f)
+        if !iip && prob.u0 isa Number
+            g! = (du, u) -> (du .= prob.f.jac(first(u), p); Cint(0))
+        elseif !iip && prob.u0 isa Vector{Float64}
+            g! = (du, u) -> (du .= prob.f.jac(u, p); Cint(0))
+        elseif !iip && prob.u0 isa AbstractArray
+            g! = (du, u) -> (du .= vec(prob.f.jac(reshape(u, sizeu), p)); Cint(0))
+        elseif prob.u0 isa Vector{Float64}
+            g! = (du, u) -> prob.f.jac(du, u, p)
+        else # Then it's an in-place function on an abstract array
+            g! = function (du, u)
+                prob.f.jac(reshape(du, size_jac), reshape(u, sizeu), p)
+                return Cint(0)
+            end
+        end
+        if prob.f.jac_prototype !== nothing
+            J = zero(prob.f.jac_prototype)
+            df = OnceDifferentiable(f!, g!, u0, resid, J)
+        else
+            df = OnceDifferentiable(f!, g!, u0, resid)
+        end
+    else
+        df = OnceDifferentiable(f!, u0, resid; autodiff)
+    end
+
+    original = nlsolve(df, u0; ftol = abstol, iterations = maxiters, method, store_trace,
+        extended_trace, linesearch, linsolve, factor, autoscale, m, beta, show_trace)
+
+    u = reshape(original.zero, size(u0))
+    f!(resid, u)
+    retcode = original.x_converged || original.f_converged ? ReturnCode.Success :
+              ReturnCode.Failure
+    stats = SciMLBase.NLStats(original.f_calls, original.g_calls, original.g_calls,
+        original.g_calls, original.iterations)
+    return SciMLBase.build_solution(prob, alg, u, resid; retcode, original, stats)
+end
+
 end

src/extension_algs.jl

@@ -143,10 +143,10 @@ function CMINPACK(; show_trace::Bool = false, tracing::Bool = false, method::Sym
 end
 
 """
-    NLSolveJL(; method=:trust_region, autodiff=:central, store_trace=false,
-          extended_trace=false, linesearch=LineSearches.Static(),
-          linsolve=(x, A, b) -> copyto!(x, A\\b), factor = one(Float64), autoscale=true,
-          m=10, beta=one(Float64), show_trace=false)
+    NLsolveJL(; method=:trust_region, autodiff=:central, store_trace=false,
+        extended_trace=false, linesearch=LineSearches.Static(),
+        linsolve=(x, A, b) -> copyto!(x, A\\b), factor = one(Float64), autoscale=true,
+        m=10, beta=one(Float64), show_trace=false)
 
 ### Keyword Arguments
 
@@ -171,7 +171,7 @@ end
 
 ### Submethod Choice
 
-Choices for methods in `NLSolveJL`:
+Choices for methods in `NLsolveJL`:
 
   - `:anderson`: Anderson-accelerated fixed-point iteration
   - `:broyden`: Broyden's quasi-Newton method
@@ -180,7 +180,7 @@ Choices for methods in `NLSolveJL`:
     these arguments, consult the
     [NLsolve.jl documentation](https://github.com/JuliaNLSolvers/NLsolve.jl).
 """
-@concrete struct NLSolveJL <: AbstractNonlinearAlgorithm
+@concrete struct NLsolveJL <: AbstractNonlinearAlgorithm
     method::Symbol
     autodiff::Symbol
     store_trace::Bool
@@ -194,14 +194,14 @@ Choices for methods in `NLSolveJL`:
     show_trace::Bool
 end
 
-function NLSolveJL(; method = :trust_region, autodiff = :central, store_trace = false,
+function NLsolveJL(; method = :trust_region, autodiff = :central, store_trace = false,
         extended_trace = false, linesearch = LineSearches.Static(),
         linsolve = (x, A, b) -> copyto!(x, A \ b), factor = 1.0, autoscale = true, m = 10,
         beta = one(Float64), show_trace = false)
     if Base.get_extension(@__MODULE__, :NonlinearSolveNLsolveExt) === nothing
-        error("NLSolveJL requires NLsolve.jl to be loaded")
+        error("NLsolveJL requires NLsolve.jl to be loaded")
     end
 
-    return NLSolveJL(method, autodiff, store_trace, extended_trace, linesearch, linsolve,
+    return NLsolveJL(method, autodiff, store_trace, extended_trace, linesearch, linsolve,
         factor, autoscale, m, beta, show_trace)
 end

test/nlsolve.jl

@@ -9,7 +9,7 @@ u0 = zeros(2)
 prob_iip = SteadyStateProblem(f_iip, u0)
 abstol = 1e-8
 
-for alg in [NLSolveJL()]
+for alg in [NLsolveJL()]
     sol = solve(prob_iip, alg)
     @test sol.retcode == ReturnCode.Success
     p = nothing
@@ -24,7 +24,7 @@ f_oop(u, p, t) = [2 - 2u[1], u[1] - 4u[2]]
 u0 = zeros(2)
 prob_oop = SteadyStateProblem(f_oop, u0)
 
-for alg in [NLSolveJL()]
+for alg in [NLsolveJL()]
     sol = solve(prob_oop, alg)
     @test sol.retcode == ReturnCode.Success
     # test the solver is doing reasonable things for linear solve
@@ -45,7 +45,7 @@ end
 u0 = zeros(2)
 prob_iip = NonlinearProblem{true}(f_iip, u0)
 abstol = 1e-8
-for alg in [NLSolveJL()]
+for alg in [NLsolveJL()]
     local sol
     sol = solve(prob_iip, alg)
     @test sol.retcode == ReturnCode.Success
@@ -60,7 +60,7 @@ end
 f_oop(u, p) = [2 - 2u[1], u[1] - 4u[2]]
 u0 = zeros(2)
 prob_oop = NonlinearProblem{false}(f_oop, u0)
-for alg in [NLSolveJL()]
+for alg in [NLsolveJL()]
     local sol
     sol = solve(prob_oop, alg)
     @test sol.retcode == ReturnCode.Success
@@ -74,7 +74,7 @@ end
 f_tol(u, p) = u^2 - 2
 prob_tol = NonlinearProblem(f_tol, 1.0)
 for tol in [1e-1, 1e-3, 1e-6, 1e-10, 1e-15]
-    sol = solve(prob_tol, NLSolveJL(), abstol = tol)
+    sol = solve(prob_tol, NLsolveJL(), abstol = tol)
     @test abs(sol.u[1] - sqrt(2)) < tol
 end
 
@@ -85,7 +85,7 @@ function f!(fvec, x, p)
 end
 
 prob = NonlinearProblem{true}(f!, [0.1; 1.2])
-sol = solve(prob, NLSolveJL(autodiff = :central))
+sol = solve(prob, NLsolveJL(autodiff = :central))
 
 du = zeros(2)
 f!(du, sol.u, nothing)
@@ -98,7 +98,7 @@ function f!(fvec, x, p)
 end
 
 prob = NonlinearProblem{true}(f!, [0.1; 1.2])
-sol = solve(prob, NLSolveJL(autodiff = :forward))
+sol = solve(prob, NLsolveJL(autodiff = :forward))
 
 du = zeros(2)
 f!(du, sol.u, nothing)
@@ -131,8 +131,8 @@ f = NonlinearFunction(f!, jac = j!)
 p = A
 
 ProbN = NonlinearProblem(f, init, p)
-sol = solve(ProbN, NLSolveJL(), reltol = 1e-8, abstol = 1e-8)
+sol = solve(ProbN, NLsolveJL(), reltol = 1e-8, abstol = 1e-8)
 
 init = ones(Complex{Float64}, 152);
 ProbN = NonlinearProblem(f, init, p)
-sol = solve(ProbN, NLSolveJL(), reltol = 1e-8, abstol = 1e-8)
+sol = solve(ProbN, NLsolveJL(), reltol = 1e-8, abstol = 1e-8)