ExpensiveOptimBenchmark
Gradient-Free-Optimizers
| ExpensiveOptimBenchmark | Gradient-Free-Optimizers | |
|---|---|---|
| 1 | 11 | |
| 19 | 1,103 | |
| - | - | |
| 3.9 | 5.0 | |
| 7 months ago | 30 days ago | |
| Python | Python | |
| MIT License | MIT License |
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ExpensiveOptimBenchmark
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29 Python real world optimization tutorials
For the problems with continous decision variables it is not trivial to come up with faster approaches on a modern many-core CPU. But even with discrete input (scheduling and planning) new continous optimizers can compete. The trick is to utilize parallel optimization runs and numba to perform around 1E6 fitness evaluations each second. Advantage is that it is much easier to create a fitness function than for instance to implement incremental score calculation in Optaplanner. And it is more flexible if you have to handle non-standard problems. For very expensive optimizations (like https://github.com/AlgTUDelft/ExpensiveOptimBenchmark) parallelization of fitness evaluation is more important than to use surrogate models.
Gradient-Free-Optimizers
- Show HN: Gradient-Free-Optimizers supports constrained optimization in v1.3
- Gradient-Free-Optimizers version 1.2 released
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Gradient-Free-Optimizers A collection of modern optimization methods in Python
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
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Hacker News top posts: Feb 28, 2021
Gradient-Free-Optimizers A collection of modern optimization methods in Python\ (0 comments)
- SimonBlanke/Gradient-Free-Optimizers A collection of modern optimization methods in Python
- Gradient-Free-Optimizers: A collection of modern optimization methods in Python
- Optimize any Python function with modern algorithms in numerical search spaces
What are some alternatives?
fast-cma-es - A Python 3 gradient-free optimization library
Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
parmoo - Python library for parallel multiobjective simulation optimization
opytimizer - 🐦 Opytimizer is a Python library consisting of meta-heuristic optimization algorithms.
surrogate-models - A collection of surrogate models for sequence model based optimization techniques
pybobyqa - Python-based Derivative-Free Optimization with Bound Constraints
optimization-tutorial - Tutorials for the optimization techniques used in Gradient-Free-Optimizers and Hyperactive.
urh - Universal Radio Hacker: Investigate Wireless Protocols Like A Boss
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.
PSO-cont-sched - Made for a college project, this Java program attempts to demonstrate how PSO might be used to solve container scheduling problems.
RocketLander - A simple framework equipped with optimization algorithms, such as reinforcement learning, evolution strategies, genetic optimization, and simulated annealing, to enable an orbital rocket booster to land autonomously.
Signal-Desktop - A private messenger for Windows, macOS, and Linux.