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

• Gradient-Free-Optimizers

  11 1,103 5.0 Python

  Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

I would be very disappointed if that were the case.. no, it looks like it’s set up to capture variance. The BO algo wraps an “Expected Improvement Optimizer”:

https://github.com/SimonBlanke/Gradient-Free-Optimizers/blob...

Which selects new points based on both the model’s mean estimate and its variance. See around line 58

• opytimizer

  7 594 5.5 Python

  🐦 Opytimizer is a Python library consisting of meta-heuristic optimization algorithms.

There's also Opytimizer [0] for almost every metaheuristic optimization algorithm under the Sun.

[0] https://github.com/gugarosa/opytimizer

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• Hyperactive

  8 490 7.7 Python

  An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.

Gradient-Free-Optimizers is a lightweight optimization package that serves as a backend for Hyperactive: https://github.com/SimonBlanke/Hyperactive

Hyperactive can do parallel computing with multiprocessing or joblib, or a custom wrapper-function.

• pybobyqa

  1 71 5.8 Python

  Python-based Derivative-Free Optimization with Bound Constraints

  

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.

• optimization-tutorial

  1 17 0.0 Python

  Tutorials for the optimization techniques used in Gradient-Free-Optimizers and Hyperactive.

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.

• sqpdfo

  1 13 0.0 Python

  Sequential-Quadratic-Programming Derivative-Free Optimization

  

If you have an expensive but not high dimensional problem you might want to try https://github.com/DLR-SC/sqpdfo .

• BayesianOptimization

  5 7,499 5.5 Python

  A Python implementation of global optimization with gaussian processes.

  

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

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• surrogate-models

  1 0 0.0 Python

  A collection of surrogate models for sequence model based optimization techniques

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

Suggest a related project