surrogate-models
pybobyqa
| surrogate-models | pybobyqa | |
|---|---|---|
| 1 | 1 | |
| 0 | 72 | |
| - | - | |
| 0.0 | 5.8 | |
| about 3 years ago | about 1 month ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 only |
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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
pybobyqa
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Gradient-Free-Optimizers A collection of modern optimization methods in Python
I've used this, and it works nicely: https://github.com/numericalalgorithmsgroup/pybobyqa. I'd be happy if it were added to your project, then I could just use yours and have access to a bunch of alternatives with the same API.
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.
tf-quant-finance - High-performance TensorFlow library for quantitative finance.
prima - PRIMA is a package for solving general nonlinear optimization problems without using derivatives. It provides the reference implementation for Powell's derivative-free optimization methods, i.e., COBYLA, UOBYQA, NEWUOA, BOBYQA, and LINCOA. PRIMA means Reference Implementation for Powell's methods with Modernization and Amelioration, P for Powell.
optimization-tutorial - Tutorials for the optimization techniques used in Gradient-Free-Optimizers and Hyperactive.
PyGenetic - A multi-purpose genetic algorithm written in python
opytimizer - 🐦 Opytimizer is a Python library consisting of meta-heuristic optimization algorithms.
WaveNCC - An app to compute the normalization coefficients of a given set of orthogonal 1D complex wave functions.