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avik-palavik-pal
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Pull in MINPACK
1 parent a0788dd commit 6b58f42

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lines changed

‎Project.toml

Lines changed: 10 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -33,12 +33,16 @@ UnPack = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
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BandedMatrices = "aae01518-5342-5314-be14-df237901396f"
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FastLevenbergMarquardt = "7a0df574-e128-4d35-8cbd-3d84502bf7ce"
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LeastSquaresOptim = "0fc2ff8b-aaa3-5acd-a817-1944a5e08891"
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MINPACK = "4854310b-de5a-5eb6-a2a5-c1dee2bd17f9"
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NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56"
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Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
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[extensions]
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NonlinearSolveBandedMatricesExt = "BandedMatrices"
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NonlinearSolveFastLevenbergMarquardtExt = "FastLevenbergMarquardt"
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NonlinearSolveLeastSquaresOptimExt = "LeastSquaresOptim"
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NonlinearSolveMINPACKExt = "MINPACK"
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NonlinearSolveNLsolveExt = "NLsolve"
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NonlinearSolveZygoteExt = "Zygote"
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[compat]
@@ -60,7 +64,9 @@ LeastSquaresOptim = "0.8"
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LineSearches = "7"
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LinearAlgebra = "<0.0.1, 1"
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LinearSolve = "2.12"
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MINPACK = "1.2"
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MaybeInplace = "0.1"
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NLsolve = "4.5"
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NaNMath = "1"
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NonlinearProblemLibrary = "0.1"
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Pkg = "1"
@@ -94,17 +100,19 @@ ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
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LeastSquaresOptim = "0fc2ff8b-aaa3-5acd-a817-1944a5e08891"
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LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
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MINPACK = "4854310b-de5a-5eb6-a2a5-c1dee2bd17f9"
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NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56"
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NaNMath = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
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NonlinearProblemLibrary = "b7050fa9-e91f-4b37-bcee-a89a063da141"
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Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
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Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
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SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
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StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
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SparseDiffTools = "47a9eef4-7e08-11e9-0b38-333d64bd3804"
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StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
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StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
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Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
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Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
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Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
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[targets]
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test = ["Aqua", "Enzyme", "BenchmarkTools", "SafeTestsets", "Pkg", "Test", "ForwardDiff", "StaticArrays", "Symbolics", "LinearSolve", "Random", "LinearAlgebra", "Zygote", "SparseDiffTools", "NonlinearProblemLibrary", "LeastSquaresOptim", "FastLevenbergMarquardt", "NaNMath", "BandedMatrices", "DiffEqBase", "StableRNGs"]
118+
test = ["Aqua", "Enzyme", "BenchmarkTools", "SafeTestsets", "Pkg", "Test", "ForwardDiff", "StaticArrays", "Symbolics", "LinearSolve", "Random", "LinearAlgebra", "Zygote", "SparseDiffTools", "NonlinearProblemLibrary", "LeastSquaresOptim", "FastLevenbergMarquardt", "NaNMath", "BandedMatrices", "DiffEqBase", "StableRNGs", "MINPACK", "NLsolve"]

‎docs/Project.toml

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Original file line numberDiff line numberDiff line change
@@ -8,10 +8,8 @@ IncompleteLU = "40713840-3770-5561-ab4c-a76e7d0d7895"
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LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
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ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
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NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
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NonlinearSolveMINPACK = "c100e077-885d-495a-a2ea-599e143bf69d"
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Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
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SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
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SciMLNLSolve = "e9a6253c-8580-4d32-9898-8661bb511710"
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SimpleNonlinearSolve = "727e6d20-b764-4bd8-a329-72de5adea6c7"
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StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
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SteadyStateDiffEq = "9672c7b4-1e72-59bd-8a11-6ac3964bc41f"
@@ -31,7 +29,6 @@ NonlinearSolve = "3"
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NonlinearSolveMINPACK = "0.1"
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Random = "<0.0.1, 1"
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SciMLBase = "2.4"
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SciMLNLSolve = "0.1"
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SimpleNonlinearSolve = "1"
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StaticArrays = "1"
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SteadyStateDiffEq = "2"

‎docs/make.jl

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Original file line numberDiff line numberDiff line change
@@ -1,5 +1,6 @@
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using Documenter, NonlinearSolve, SimpleNonlinearSolve, Sundials, SciMLNLSolve,
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NonlinearSolveMINPACK, SteadyStateDiffEq, SciMLBase, DiffEqBase
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using Documenter,
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NonlinearSolve, SimpleNonlinearSolve, Sundials,
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SteadyStateDiffEq, SciMLBase, DiffEqBase
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cp("./docs/Manifest.toml", "./docs/src/assets/Manifest.toml", force = true)
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cp("./docs/Project.toml", "./docs/src/assets/Project.toml", force = true)
@@ -9,7 +10,7 @@ include("pages.jl")
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makedocs(sitename = "NonlinearSolve.jl",
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authors = "Chris Rackauckas",
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modules = [NonlinearSolve, SciMLBase, DiffEqBase, SimpleNonlinearSolve, Sundials,
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SciMLNLSolve, NonlinearSolveMINPACK, SteadyStateDiffEq],
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SteadyStateDiffEq],
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clean = true, doctest = false, linkcheck = true,
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linkcheck_ignore = ["https://twitter.com/ChrisRackauckas/status/1544743542094020615"],
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warnonly = [:missing_docs, :cross_references],

‎docs/src/api/fastlevenbergmarquardt.md

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@@ -1,17 +1,15 @@
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# FastLevenbergMarquardt.jl
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3-
This is a wrapper package for importing solvers from FastLevenbergMarquardt.jl into the
4-
SciML interface. Note that these solvers do not come by default, and thus one needs to
5-
install the package before using these solvers:
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This is a extension for importing solvers from FastLevenbergMarquardt.jl into the SciML
4+
interface. Note that these solvers do not come by default, and thus one needs to install
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the package before using these solvers:
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77
```julia
88
using Pkg
99
Pkg.add("FastLevenbergMarquardt")
10-
using FastLevenbergMarquardt
10+
using FastLevenbergMarquardt, NonlinearSolve
1111
```
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13-
These methods can be used independently of the rest of NonlinearSolve.jl
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## Solver API
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```@docs

‎docs/src/api/leastsquaresoptim.md

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@@ -1,17 +1,15 @@
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# LeastSquaresOptim.jl
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This is a wrapper package for importing solvers from LeastSquaresOptim.jl into the SciML
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This is a extension for importing solvers from LeastSquaresOptim.jl into the SciML
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interface. Note that these solvers do not come by default, and thus one needs to install
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the package before using these solvers:
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```julia
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using Pkg
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Pkg.add("LeastSquaresOptim")
10-
using LeastSquaresOptim
10+
using LeastSquaresOptim, NonlinearSolve
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```
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These methods can be used independently of the rest of NonlinearSolve.jl
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## Solver API
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```@docs

‎docs/src/api/minpack.md

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@@ -1,17 +1,15 @@
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# MINPACK.jl
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This is a wrapper package for importing solvers from Sundials into the SciML interface.
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Note that these solvers do not come by default, and thus one needs to install the package
5-
before using these solvers:
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This is a extension for importing solvers from MINPACK into the SciML interface. Note that
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these solvers do not come by default, and thus one needs to install the package before using
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these solvers:
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```julia
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using Pkg
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Pkg.add("NonlinearSolveMINPACK")
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using NonlinearSolveMINPACK
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Pkg.add("MINPACK")
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using MINPACK, NonlinearSolve
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```
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These methods can be used independently of the rest of NonlinearSolve.jl
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## Solver API
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```@docs

‎docs/src/api/nlsolve.md

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@@ -1,13 +1,13 @@
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# NLsolve.jl
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This is a wrapper package for importing solvers from NLsolve.jl into the SciML interface.
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Note that these solvers do not come by default, and thus one needs to install the package
5-
before using these solvers:
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This is a extension for importing solvers from NLsolve.jl into the SciML interface. Note
4+
that these solvers do not come by default, and thus one needs to install the package before
5+
using these solvers:
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77
```julia
88
using Pkg
9-
Pkg.add("SciMLNLSolve")
10-
using SciMLNLSolve
9+
Pkg.add("NLsolve")
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using NLSolve, NonlinearSolve
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```
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## Solver API

‎docs/src/solvers/NonlinearLeastSquaresSolvers.md

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@@ -58,6 +58,23 @@ And the choices for `linsolve` are:
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- `:cholesky`
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- `:lsmr` a conjugate gradient method (LSMR with diagonal preconditioner).
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61+
### MINPACK.jl
62+
63+
MINPACK.jl methods are fine for medium-sized nonlinear solves. They are the FORTRAN
64+
standard methods which are used in many places, such as SciPy. However, our benchmarks
65+
demonstrate that these methods are not robust or stable. In addition, they are slower
66+
than the standard methods and do not scale due to lack of sparse Jacobian support.
67+
Thus they are only recommended for benchmarking and testing code conversions.
68+
69+
- `CMINPACK()`: A wrapper for using the classic MINPACK method through [MINPACK.jl](https://github.com/sglyon/MINPACK.jl)
70+
71+
Submethod choices for this algorithm include:
72+
73+
- `:hybr`: Modified version of Powell's algorithm.
74+
- `:lm`: Levenberg-Marquardt.
75+
- `:lmdif`: Advanced Levenberg-Marquardt
76+
- `:hybrd`: Advanced modified version of Powell's algorithm
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### Optimization.jl
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`NonlinearLeastSquaresProblem`s can be converted into an `OptimizationProblem` and used

‎docs/src/solvers/NonlinearSystemSolvers.md

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@@ -110,7 +110,7 @@ computationally expensive than direct methods.
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close to the steady state.
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- `SSRootfind()`: Uses a NonlinearSolve compatible solver to find the steady state.
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113-
### SciMLNLSolve.jl
113+
### NLsolve.jl
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115115
This is a wrapper package for importing solvers from NLsolve.jl into the SciML interface.
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‎ext/NonlinearSolveMINPACKExt.jl

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module NonlinearSolveMINPACKExt
2+
3+
using NonlinearSolve, SciMLBase
4+
using MINPACK
5+
6+
function SciMLBase.__solve(prob::Union{NonlinearProblem{uType, iip},
7+
NonlinearLeastSquaresProblem{uType, iip}}, alg::CMINPACK, args...;
8+
abstol = 1e-6, maxiters = 100000, alias_u0::Bool = false,
9+
kwargs...) where {uType, iip}
10+
if prob.u0 isa Number
11+
u0 = [prob.u0]
12+
else
13+
u0 = NonlinearSolve.__maybe_unaliased(prob.u0, alias_u0)
14+
end
15+
16+
sizeu = size(prob.u0)
17+
p = prob.p
18+
19+
# unwrapping alg params
20+
show_trace = alg.show_trace
21+
tracing = alg.tracing
22+
io = alg.io
23+
24+
if !iip && prob.u0 isa Number
25+
f! = (du, u) -> (du .= prob.f(first(u), p); Cint(0))
26+
elseif !iip && prob.u0 isa Vector{Float64}
27+
f! = (du, u) -> (du .= prob.f(u, p); Cint(0))
28+
elseif !iip && prob.u0 isa AbstractArray
29+
f! = (du, u) -> (du .= vec(prob.f(reshape(u, sizeu), p)); Cint(0))
30+
elseif prob.u0 isa Vector{Float64}
31+
f! = (du, u) -> prob.f(du, u, p)
32+
else # Then it's an in-place function on an abstract array
33+
f! = (du, u) -> (prob.f(reshape(du, sizeu), reshape(u, sizeu), p); du = vec(du); 0)
34+
end
35+
36+
u = zero(u0)
37+
resid = NonlinearSolve.evaluate_f(prob, u)
38+
m = length(resid)
39+
40+
method = ifelse(alg.method === :auto,
41+
ifelse(prob isa NonlinearLeastSquaresProblem, :lm, :hydr), alg.method)
42+
43+
if SciMLBase.has_jac(prob.f)
44+
if !iip && prob.u0 isa Number
45+
g! = (du, u) -> (du .= prob.f.jac(first(u), p); Cint(0))
46+
elseif !iip && prob.u0 isa Vector{Float64}
47+
g! = (du, u) -> (du .= prob.f.jac(u, p); Cint(0))
48+
elseif !iip && prob.u0 isa AbstractArray
49+
g! = (du, u) -> (du .= vec(prob.f.jac(reshape(u, sizeu), p)); Cint(0))
50+
elseif prob.u0 isa Vector{Float64}
51+
g! = (du, u) -> prob.f.jac(du, u, p)
52+
else # Then it's an in-place function on an abstract array
53+
g! = function (du, u)
54+
prob.f.jac(reshape(du, sizeu), reshape(u, sizeu), p)
55+
du = vec(du)
56+
return CInt(0)
57+
end
58+
end
59+
original = MINPACK.fsolve(f!, g!, u0, m; tol = abstol, show_trace, tracing, method,
60+
iterations = maxiters, kwargs...)
61+
else
62+
original = MINPACK.fsolve(f!, u0, m; tol = abstol, show_trace, tracing, method,
63+
iterations = maxiters, kwargs...)
64+
end
65+
66+
u = reshape(original.x, size(u))
67+
resid = original.f
68+
retcode = original.converged ? ReturnCode.Success : ReturnCode.Failure
69+
70+
return SciMLBase.build_solution(prob, alg, u, resid; retcode, original)
71+
end
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73+
end

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