Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/SciML/NonlinearSolve.jl
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
https://github.com/SciML/NonlinearSolve.jl
bracketing deep-equilibrium-models differential-equations equilibrium factorization high-performance-computing julia newton-krylov newton-method newton-raphson nonlinear-equations scientific-machine-learning sciml sparse-matrices sparse-matrix steady-state
Last synced: 3 months ago
JSON representation
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
- Host: GitHub
- URL: https://github.com/SciML/NonlinearSolve.jl
- Owner: SciML
- License: mit
- Created: 2020-08-30T10:54:01.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2024-07-17T16:25:23.000Z (4 months ago)
- Last Synced: 2024-07-22T22:44:20.995Z (4 months ago)
- Topics: bracketing, deep-equilibrium-models, differential-equations, equilibrium, factorization, high-performance-computing, julia, newton-krylov, newton-method, newton-raphson, nonlinear-equations, scientific-machine-learning, sciml, sparse-matrices, sparse-matrix, steady-state
- Language: Julia
- Homepage: https://docs.sciml.ai/NonlinearSolve/stable/
- Size: 19.8 MB
- Stars: 220
- Watchers: 13
- Forks: 39
- Open Issues: 38
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
- awesome-sciml - SciML/NonlinearSolve.jl: High-performance and differentiation-enabled nonlinear solvers
README
# NonlinearSolve.jl
[![Join the chat at https://julialang.zulipchat.com #sciml-bridged](https://img.shields.io/static/v1?label=Zulip&message=chat&color=9558b2&labelColor=389826)](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged)
[![Global Docs](https://img.shields.io/badge/docs-SciML-blue.svg)](https://docs.sciml.ai/NonlinearSolve/stable/)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10397607.svg)](https://doi.org/10.5281/zenodo.10397607)[![codecov](https://codecov.io/gh/SciML/NonlinearSolve.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/SciML/NonlinearSolve.jl)
[![Build Status](https://github.com/SciML/NonlinearSolve.jl/workflows/CI/badge.svg)](https://github.com/SciML/NonlinearSolve.jl/actions?query=workflow%3ACI)
[![Build status](https://badge.buildkite.com/413dc8df7d555cc14c262aba066503a9e7a42023f9cfb75a55.svg?branch=master)](https://buildkite.com/julialang/nonlinearsolve-dot-jl)[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor%27s%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
[![SciML Code Style](https://img.shields.io/static/v1?label=code%20style&message=SciML&color=9558b2&labelColor=389826)](https://github.com/SciML/SciMLStyle)Fast implementations of root finding algorithms in Julia that satisfy the SciML common interface.
For information on using the package,
[see the stable documentation](https://docs.sciml.ai/NonlinearSolve/stable/). Use the
[in-development documentation](https://docs.sciml.ai/NonlinearSolve/dev/) for the version of
the documentation which contains the unreleased features.## High Level Examples
```julia
using NonlinearSolve, StaticArraysf(u, p) = u .* u .- 2
u0 = @SVector[1.0, 1.0]
prob = NonlinearProblem(f, u0)
solver = solve(prob)## Bracketing Methods
f(u, p) = u .* u .- 2.0
u0 = (1.0, 2.0) # brackets
prob = IntervalNonlinearProblem(f, u0)
sol = solve(prob)
```## Citation
If you found this library to be useful in academic work, then please cite:
```bibtex
@article{pal2024nonlinearsolve,
title={NonlinearSolve. jl: High-Performance and Robust Solvers for Systems of Nonlinear Equations in Julia},
author={Pal, Avik and Holtorf, Flemming and Larsson, Axel and Loman, Torkel and Schaefer, Frank and Qu, Qingyu and Edelman, Alan and Rackauckas, Chris and others},
journal={arXiv preprint arXiv:2403.16341},
year={2024}
}
```