SciML / Optimization.jl

Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
https://docs.sciml.ai/Optimization/stable/
MIT License
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Add more polyalgorithms such as mixture of global (BBO/CMAES/...) and first order and second order optimizers #523

Closed Vaibhavdixit02 closed 3 months ago

Vaibhavdixit02 commented 1 year ago

OptimizationPolyAlgorithms can become the primary choice by adding these methods, pretty low hanging fruit

Vaibhavdixit02 commented 3 months ago

This will never be comprehensive enough and the remake documentation should take care of it. We might want to have some suggestions of things that work well but unless there's real fundamental reason to run two solvers one after the other this won't be worked on, but that'll have to be a solver implementation directly and not necessarily this approach of automagically stringing things together