Gradient Free Optimizers: A collection of modern optimization methods in Python

Re:Gradient Free Optimizers: A collection of modern optimiza... by MauranKilom on Sunday February 28, 2021 @01:49PM (#26295926)

I'd recommend also implementing the DIRECT algorithm, which balances local and global search very well (but consequently will not refine as aggressively near the current [possibly only local] optimum as other algorithms):

https://www.researchgate.net/profile/Donald-Jones-5/publicat...

You probably also want to include some advantages/disadvantages of each algorithm. How robust against local minima is it? Up to how many dimensions does it work well? How is the convergence speed when started far from the optimum? Does it work well with few function evaluations? Etc.

[ Parent | on HN ]

Re:Gradient Free Optimizers: A collection of modern optimiza... by simonblanke on Sunday February 28, 2021 @03:55PM (#26296979)

I will look into this algorithm. Thanks for the suggestion. I have some basic explanations of the optimization techniques and their parameters in a separate repository: https://github.com/SimonBlanke/optimization-tutorial

But there is still a lot of work to be done.

[ Parent | on HN ]