pybobyqa
BayesianOptimization
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pybobyqa | BayesianOptimization | |
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1 | 5 | |
71 | 7,499 | |
- | 2.0% | |
5.8 | 5.5 | |
19 days ago | 6 days ago | |
Python | Python | |
GNU General Public License v3.0 only | MIT License |
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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.
BayesianOptimization
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How best to compress a list of objective function evaluations in numerical optimization?
Yes but that’s a pretty broad label- is there a specific implementation you’re working with (for example ) that pinpoints the memory overhead you want to shrink?
- It's so fun and useful to me
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[P] Bonsai: Bayesian Optimization for Gradient Boosted Trees
Sure, I’m only aware of the Bayesian Optimization package (https://github.com/fmfn/BayesianOptimization), but if you can recommend some other GP-based methods that integrate well with Gradient boosted machines, that would be nice.
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How to optimize multiple variables to minimize the output?
I've previously used Bayesian optimisation for this kind of problem, if you're working in python this is a pretty great starting point (https://github.com/fmfn/BayesianOptimization). Black box optimisation is, to the best of my knowledge, a pretty large field and certainly a very difficult problem. You could certainly do a lot worse than BayesOpt.
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Gradient-Free-Optimizers A collection of modern optimization methods in Python
This looks super interesting, I have previously considered using the Bayesian Optimization[0] package for some work, but the ability to switch out the underlying algorithms is appealing.
Perhaps a bit of a far out question - I would be interested in using this for optimizing real-world (ie slow, expensive, noisy) processes. A caveat with this is that the work is done in batches (eg N experiments at a time). Is there a mechanism by which I could feed in my results from previous rounds and have the algorithm suggest the next N configurations that are sufficiently uncorrelated to explore promising space without bunching on top of each-other? My immediate read is that I could use the package to pick the next optimal point, but would then have to lean on a random search for the remainder of the batch?
0: https://github.com/fmfn/BayesianOptimization
What are some alternatives?
Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
opytimizer - 🐦 Opytimizer is a Python library consisting of meta-heuristic optimization algorithms.
tf-quant-finance - High-performance TensorFlow library for quantitative finance.
nhentai-favorites-auto-pagination - This is an infinity randomly picker doujinshi from yours favorite list with auto scroll and pagination
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.
ix - Simple dotfile pre-processor with a per-file configuration and no dependencies.
PyGenetic - A multi-purpose genetic algorithm written in python
WaveNCC - An app to compute the normalization coefficients of a given set of orthogonal 1D complex wave functions.
optimization-tutorial - Tutorials for the optimization techniques used in Gradient-Free-Optimizers and Hyperactive.
Gradient-Free-Optimizers - Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
surrogate-models - A collection of surrogate models for sequence model based optimization techniques