SciML/NonlinearSolve.jl

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

Juliajuliahigh-performance-computingdifferential-equationsfactorizationnonlinear-equationssparse-matrixsparse-matricesnewton-raphsonsteady-statebracketingequilibriumnewton-methodscientific-machine-learningscimlnewton-krylovdeep-equilibrium-models
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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 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] probN = NonlinearProblem(f, u0) solver = solve(probN, NewtonRaphson(), abstol = 1e-9) ## Bracketing Methods f(u,...
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