Merge branch 'master' into qqy/siam_ext

Author: ErikQQY

Project.toml

@@ -1,7 +1,7 @@
 name = "NonlinearSolve"
 uuid = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
 authors = ["SciML"]
-version = "3.1.2"
+version = "3.2.0"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
@@ -32,27 +32,31 @@ UnPack = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
 [weakdeps]
 BandedMatrices = "aae01518-5342-5314-be14-df237901396f"
 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"
 SIAMFANLEquations = "084e46ad-d928-497d-ad5e-07fa361a48c4"
+SpeedMapping = "f1835b91-879b-4a3f-a438-e4baacf14412"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [extensions]
 NonlinearSolveBandedMatricesExt = "BandedMatrices"
 NonlinearSolveFastLevenbergMarquardtExt = "FastLevenbergMarquardt"
+NonlinearSolveFixedPointAccelerationExt = "FixedPointAcceleration"
 NonlinearSolveLeastSquaresOptimExt = "LeastSquaresOptim"
 NonlinearSolveMINPACKExt = "MINPACK"
 NonlinearSolveNLsolveExt = "NLsolve"
 NonlinearSolveSIAMFANLEquationsExt = "SIAMFANLEquations"
+NonlinearSolveSpeedMappingExt = "SpeedMapping"
 NonlinearSolveSymbolicsExt = "Symbolics"
 NonlinearSolveZygoteExt = "Zygote"
 
 [compat]
 ADTypes = "0.2.5"
 Aqua = "0.8"
-ArrayInterface = "7.6"
+ArrayInterface = "7.7"
 BandedMatrices = "1.4"
 BenchmarkTools = "1.4"
 ConcreteStructs = "0.2"
@@ -62,6 +66,7 @@ Enzyme = "0.11.11"
 FastBroadcast = "0.2.8"
 FastLevenbergMarquardt = "0.1"
 FiniteDiff = "2.21"
+FixedPointAcceleration = "0.3"
 ForwardDiff = "0.10.36"
 LazyArrays = "1.8.2"
 LeastSquaresOptim = "0.8.5"
@@ -73,20 +78,21 @@ MaybeInplace = "0.1.1"
 NLsolve = "4.5"
 NaNMath = "1"
 NonlinearProblemLibrary = "0.1.1"
-OrdinaryDiffEq = "6"
+OrdinaryDiffEq = "6.63"
 Pkg = "1"
 PrecompileTools = "1.2"
-Printf = "<0.0.1, 1"
-Random = "<0.0.1, 1"
-RecursiveArrayTools = "3.0"
+Printf = "1.9"
+Random = "1.91"
+RecursiveArrayTools = "3.2"
 Reexport = "1.2"
 SafeTestsets = "0.1"
 SciMLBase = "2.11"
 SciMLOperators = "0.3.7"
 SIAMFANLEquations = "1.0.1"
 SimpleNonlinearSolve = "1.0.2"
-SparseArrays = "<0.0.1, 1"
+SparseArrays = "1.9"
 SparseDiffTools = "2.14"
+SpeedMapping = "0.3"
 StableRNGs = "1"
 StaticArrays = "1.7"
 Symbolics = "5.13"
@@ -102,6 +108,7 @@ BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e"
 Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
 FastLevenbergMarquardt = "7a0df574-e128-4d35-8cbd-3d84502bf7ce"
+FixedPointAcceleration = "817d07cb-a79a-5c30-9a31-890123675176"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 LeastSquaresOptim = "0fc2ff8b-aaa3-5acd-a817-1944a5e08891"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
@@ -116,11 +123,12 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
 SIAMFANLEquations = "084e46ad-d928-497d-ad5e-07fa361a48c4"
 SparseDiffTools = "47a9eef4-7e08-11e9-0b38-333d64bd3804"
+SpeedMapping = "f1835b91-879b-4a3f-a438-e4baacf14412"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 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", "SIAMFANLEquations"]
+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"]

docs/pages.jl

@@ -20,6 +20,7 @@ pages = ["index.md",
         "solvers/BracketingSolvers.md",
         "solvers/SteadyStateSolvers.md",
         "solvers/NonlinearLeastSquaresSolvers.md",
+        "solvers/FixedPointSolvers.md",
         "solvers/LineSearch.md"],
     "Detailed Solver APIs" => Any["api/nonlinearsolve.md",
         "api/simplenonlinearsolve.md",

docs/src/api/fixedpointacceleration.md

@@ -0,0 +1,17 @@
+# FixedPointAcceleration.jl
+
+This is a extension for importing solvers from FixedPointAcceleration.jl into the SciML
+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("FixedPointAcceleration")
+using FixedPointAcceleration, NonlinearSolve
+```
+
+## Solver API
+
+```@docs
+FixedPointAccelerationJL
+```

docs/src/api/speedmapping.md

@@ -0,0 +1,17 @@
+# SpeedMapping.jl
+
+This is a extension for importing solvers from SpeedMapping.jl into the SciML
+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("SpeedMapping")
+using SpeedMapping, NonlinearSolve
+```
+
+## Solver API
+
+```@docs
+SpeedMappingJL
+```

docs/src/solvers/FixedPointSolvers.md

@@ -0,0 +1,42 @@
+# Fixed Point Solvers
+
+Currently we don't have an API to directly specify Fixed Point Solvers. However, a Fixed
+Point Problem can be trivially converted to a Root Finding Problem. Say we want to solve:
+
+```math
+f(u) = u
+```
+
+This can be written as:
+
+```math
+g(u) = f(u) - u = 0
+```
+
+``g(u) = 0`` is a root finding problem. Note that we can use any root finding
+algorithm to solve this problem. However, this is often not the most efficient way to
+solve a fixed point problem. We provide a few algorithms available via extensions that
+are more efficient for fixed point problems.
+
+Note that even if you use one of the Fixed Point Solvers mentioned here, you must still
+use the `NonlinearProblem` API to specify the problem, i.e., ``g(u) = 0``.
+
+## Recommended Methods
+
+Using [native NonlinearSolve.jl methods](@ref nonlinearsystemsolvers) is the recommended
+approach. For systems where constructing Jacobian Matrices are expensive, we recommend
+using a Krylov Method with one of those solvers.
+
+## Full List of Methods
+
+We are only listing the methods that natively solve fixed point problems.
+
+### SpeedMapping.jl
+
+  - `SpeedMappingJL()`: accelerates the convergence of a mapping to a fixed point by the
+    Alternating cyclic extrapolation algorithm (ACX).
+
+### FixedPointAcceleration.jl
+
+  - `FixedPointAccelerationJL()`: accelerates the convergence of a mapping to a fixed point
+    by the Anderson acceleration algorithm and a few other methods.

docs/src/solvers/NonlinearSystemSolvers.md

@@ -1,6 +1,6 @@
 # [Nonlinear System Solvers](@id nonlinearsystemsolvers)
 
-`solve(prob::NonlinearProblem,alg;kwargs)`
+`solve(prob::NonlinearProblem, alg; kwargs)`
 
 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.

ext/NonlinearSolveFixedPointAccelerationExt.jl

@@ -0,0 +1,56 @@
+module NonlinearSolveFixedPointAccelerationExt
+
+using NonlinearSolve, FixedPointAcceleration, DiffEqBase, SciMLBase
+
+function SciMLBase.__solve(prob::NonlinearProblem, alg::FixedPointAccelerationJL, args...;
+        abstol = nothing, maxiters = 1000, alias_u0::Bool = false,
+        show_trace::Val{PrintReports} = Val(false), termination_condition = nothing,
+        kwargs...) where {PrintReports}
+    @assert (termination_condition ===
+             nothing)||(termination_condition isa AbsNormTerminationMode) "SpeedMappingJL does not support termination conditions!"
+
+    u0 = NonlinearSolve.__maybe_unaliased(prob.u0, alias_u0)
+    u_size = size(u0)
+    T = eltype(u0)
+    iip = isinplace(prob)
+    p = prob.p
+
+    if !iip && prob.u0 isa Number
+        # FixedPointAcceleration makes the scalar problem into a vector problem
+        f = (u) -> [prob.f(u[1], p) .+ u[1]]
+    elseif !iip && prob.u0 isa AbstractVector
+        f = (u) -> (prob.f(u, p) .+ u)
+    elseif !iip && prob.u0 isa AbstractArray
+        f = (u) -> vec(prob.f(reshape(u, u_size), p) .+ u)
+    elseif iip && prob.u0 isa AbstractVector
+        du = similar(u0)
+        f = (u) -> (prob.f(du, u, p); du .+ u)
+    else
+        du = similar(u0)
+        f = (u) -> (prob.f(du, reshape(u, u_size), p); vec(du) .+ u)
+    end
+
+    tol = abstol === nothing ? real(oneunit(T)) * (eps(real(one(T))))^(4 // 5) : abstol
+
+    sol = fixed_point(f, NonlinearSolve._vec(u0); Algorithm = alg.algorithm,
+        ConvergenceMetricThreshold = tol, MaxIter = maxiters, MaxM = alg.m,
+        ExtrapolationPeriod = alg.extrapolation_period, Dampening = alg.dampening,
+        PrintReports, ReplaceInvalids = alg.replace_invalids,
+        ConditionNumberThreshold = alg.condition_number_threshold, quiet_errors = true)
+
+    res = prob.u0 isa Number ? first(sol.FixedPoint_) : sol.FixedPoint_
+    if res === missing
+        resid = NonlinearSolve.evaluate_f(prob, u0)
+        res = u0
+        converged = false
+    else
+        resid = NonlinearSolve.evaluate_f(prob, res)
+        converged = maximum(abs, resid) ≤ tol
+    end
+    return SciMLBase.build_solution(prob, alg, res, resid;
+        retcode = converged ? ReturnCode.Success : ReturnCode.Failure,
+        stats = SciMLBase.NLStats(sol.Iterations_, 0, 0, 0, sol.Iterations_),
+        original = sol)
+end
+
+end

ext/NonlinearSolveMINPACKExt.jl

@@ -5,7 +5,7 @@ using MINPACK
 
 function SciMLBase.__solve(prob::Union{NonlinearProblem{uType, iip},
             NonlinearLeastSquaresProblem{uType, iip}}, alg::CMINPACK, args...;
-        abstol = 1e-6, maxiters = 100000, alias_u0::Bool = false,
+        abstol = nothing, maxiters = 100000, alias_u0::Bool = false,
         termination_condition = nothing, kwargs...) where {uType, iip}
     @assert (termination_condition ===
              nothing)||(termination_condition isa AbsNormTerminationMode) "CMINPACK does not support termination conditions!"
@@ -16,6 +16,7 @@ function SciMLBase.__solve(prob::Union{NonlinearProblem{uType, iip},
         u0 = NonlinearSolve.__maybe_unaliased(prob.u0, alias_u0)
     end
 
+    T = eltype(u0)
     sizeu = size(prob.u0)
     p = prob.p
 
@@ -25,11 +26,11 @@ function SciMLBase.__solve(prob::Union{NonlinearProblem{uType, iip},
 
     if !iip && prob.u0 isa Number
         f! = (du, u) -> (du .= prob.f(first(u), p); Cint(0))
-    elseif !iip && prob.u0 isa Vector{Float64}
+    elseif !iip && prob.u0 isa AbstractVector
         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}
+    elseif prob.u0 isa AbstractVector
         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)
@@ -43,14 +44,16 @@ function SciMLBase.__solve(prob::Union{NonlinearProblem{uType, iip},
     method = ifelse(alg.method === :auto,
         ifelse(prob isa NonlinearLeastSquaresProblem, :lm, :hybr), alg.method)
 
+    abstol = abstol === nothing ? real(oneunit(T)) * (eps(real(one(T))))^(4 // 5) : abstol
+
     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}
+        elseif !iip && prob.u0 isa AbstractVector
             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}
+        elseif prob.u0 isa AbstractVector
             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)

ext/NonlinearSolveNLsolveExt.jl

@@ -3,8 +3,9 @@ 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, termination_condition = nothing, kwargs...)
+function SciMLBase.__solve(prob::NonlinearProblem, alg::NLsolveJL, args...;
+        abstol = nothing, maxiters = 1000, alias_u0::Bool = false,
+        termination_condition = nothing, kwargs...)
     @assert (termination_condition ===
              nothing)||(termination_condition isa AbsNormTerminationMode) "NLsolveJL does not support termination conditions!"
 
@@ -14,6 +15,7 @@ function SciMLBase.__solve(prob::NonlinearProblem, alg::NLsolveJL, args...; abst
         u0 = NonlinearSolve.__maybe_unaliased(prob.u0, alias_u0)
     end
 
+    T = eltype(u0)
     iip = isinplace(prob)
 
     sizeu = size(prob.u0)
@@ -25,11 +27,11 @@ function SciMLBase.__solve(prob::NonlinearProblem, alg::NLsolveJL, args...; abst
 
     if !iip && prob.u0 isa Number
         f! = (du, u) -> (du .= prob.f(first(u), p); Cint(0))
-    elseif !iip && prob.u0 isa Vector{Float64}
+    elseif !iip && prob.u0 isa AbstractVector
         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}
+    elseif prob.u0 isa AbstractVector
         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)
@@ -46,11 +48,11 @@ function SciMLBase.__solve(prob::NonlinearProblem, alg::NLsolveJL, args...; abst
     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}
+        elseif !iip && prob.u0 isa AbstractVector
             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}
+        elseif prob.u0 isa AbstractVector
             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)
@@ -68,6 +70,8 @@ function SciMLBase.__solve(prob::NonlinearProblem, alg::NLsolveJL, args...; abst
         df = OnceDifferentiable(f!, vec(u0), vec(resid); autodiff)
     end
 
+    abstol = abstol === nothing ? real(oneunit(T)) * (eps(real(one(T))))^(4 // 5) : abstol
+
     original = nlsolve(df, vec(u0); ftol = abstol, iterations = maxiters, method,
         store_trace, extended_trace, linesearch, linsolve, factor, autoscale, m, beta,
         show_trace)