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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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SciML/NonlinearSolve.jl

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NonlinearSolve.jl

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ColPrac: Contributor's Guide on Collaborative Practices for Community Packages SciML Code Style

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)