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High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.

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MIT license

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NonlinearSolve.jl

Fast implementations of root finding algorithms in Julia that satisfy the SciML common interface.

For information on using the package,see the stable documentation. Use thein-development documentation for the version ofthe documentation which contains the unreleased features.

High Level Examples

using NonlinearSolve, StaticArraysf(u, p)= u.* u.-2u0=@SVector[1.0,1.0]prob=NonlinearProblem(f, u0)solver=solve(prob)## Bracketing Methodsf(u, p)= u.* u.-2.0u0= (1.0,2.0)# bracketsprob=IntervalNonlinearProblem(f, u0)sol=solve(prob)

Citation

If you found this library to be useful in academic work, then please cite:

@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},journal={arXiv preprint arXiv:2403.16341},year={2024}}

About

High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.

docs.sciml.ai/NonlinearSolve/stable/