Chris Rackauckas - NonlinearSolve.jl: Efficient Rootfinding and Algebraic Equations in Julia

Many problems can be reduced down to solving f(x) = 0, maybe even more than you think! Solving a stiff differential equation? Finding out where the ball hits the ground? Solving an inverse problem to find the parameters to fit a model? In this talk we'll showcase how SciML's NonlinearSolve.jl is a general system for solving nonlinear equations and demonstrate its ability to efficiently handle these kinds of problems with high stability and performance.
More Information: Why we deprecated NLsolve.jl for NonlinearSolve.jl for Solving Nonlinear Systems in Julia sciml.ai/news/2024/01/23/nlso...
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[@nicklam3594]

At this point it is not Julia that is good. It is the SciML guys and their mentality lol!

[@sucim]

"Came for the diffeq stuff, stayed for everything else" will be the future onboarding pipeline for new Julia users :D

[@jaantollander]

Very exciting talk as always!

[@clementdato6328]

I am not very familiar with solvers, but this seems unreasonably good. Automatic sparsity pattern seems to be a simplified version of a more general problem to detect priors or intent of a user by a system.