SciML / NonlinearSolve.jl

High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
https://docs.sciml.ai/NonlinearSolve/stable/
MIT License
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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

NonlinearSolve.jl

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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 the in-development documentation for the version of the documentation which contains the unreleased features.

High Level Examples

using NonlinearSolve, StaticArrays

f(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:

@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}
}