elkai
BayesianOptimization
| elkai | BayesianOptimization | |
|---|---|---|
| 3 | 5 | |
| 161 | 7,499 | |
| - | 1.2% | |
| 6.5 | 5.5 | |
| 12 months ago | 10 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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elkai
- GitHub - fikisipi/elkai: elkai is a Python library for solving travelling salesman problems (TSP) based on LKH 3
- Show HN: I've upgraded my travelling salesman solver from LKHv2 to LKHv3
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Tree – a lib for working with nested data structures, open-sourced by deepmind
My project builds 20 wheels supporting most platforms except ARM (todo): https://pypi.org/project/elkai/#files It's one line of code using CMake. [1]
Granted, you also need CI that has most Python versions and platforms and the scikit CMake extension too. Ex. Travis with brew [2]
[1] https://github.com/filipArena/elkai/blob/ef8ee98d8114240b204...
[2] https://github.com/filipArena/elkai/blob/master/.travis.yml
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?
tree - tree is a library for working with nested data structures
opytimizer - 🐦 Opytimizer is a Python library consisting of meta-heuristic optimization algorithms.
nhentai-favorites-auto-pagination - This is an infinity randomly picker doujinshi from yours favorite list with auto scroll and pagination
ix - Simple dotfile pre-processor with a per-file configuration and no dependencies.
optimization-tutorial - Tutorials for the optimization techniques used in Gradient-Free-Optimizers and Hyperactive.
Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
surrogate-models - A collection of surrogate models for sequence model based optimization techniques
Bayesian-Optimization-in-FSharp - Bayesian Optimization via Gaussian Processes in F#
pybobyqa - Python-based Derivative-Free Optimization with Bound Constraints
bonsai - Gradient Boosted Trees + Bayesian Optimization
DIgging - Decision Intelligence for digging best parameters in target environment.
draf - Demand Response Analysis Framework (DRAF)