surrogate-models
optimization-tutorial
| surrogate-models | optimization-tutorial | |
|---|---|---|
| 1 | 1 | |
| 0 | 17 | |
| - | - | |
| 0.0 | 0.0 | |
| about 3 years ago | about 2 years ago | |
| Python | Python | |
| MIT License | MIT License |
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surrogate-models
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Gradient-Free-Optimizers A collection of modern optimization methods in Python
Yes it is quite easy to switch algorithms via the "gpr" parameter. You just have to write a wrapper class. I am currently working on a repository that discusses how to do that in detail: https://github.com/SimonBlanke/surrogate-models
optimization-tutorial
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Gradient-Free-Optimizers A collection of modern optimization methods in Python
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.
What are some alternatives?
Gradient-Free-Optimizers - Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
BayesianOptimization - A Python implementation of global optimization with gaussian processes.
opytimizer - 🐦 Opytimizer is a Python library consisting of meta-heuristic optimization algorithms.
pybobyqa - Python-based Derivative-Free Optimization with Bound Constraints
sigopt-server - Open Source version of SigOpt API, performing hyperparameter optimization and visualization
sqpdfo - Sequential-Quadratic-Programming Derivative-Free Optimization