wujian16/Cornell-MOE
A Python library for the state-of-the-art Bayesian optimization algorithms, with the core implemented in C++.
This tool helps scientists and engineers efficiently find the best settings for experiments or simulations where each test takes a long time or is expensive to run. You input a range of possible settings, and it guides you on which specific settings to try next, ultimately outputting the optimal configuration with the fewest possible evaluations. This is ideal for researchers or anyone optimizing 'black box' systems.
275 stars. No commits in the last 6 months.
Use this if you need to find optimal settings for a continuous process where each evaluation is costly or time-consuming, and you have a limited number of input variables (typically fewer than 20).
Not ideal if your objective function evaluations are very fast and cheap, if you have a huge number of input variables, or if your constraints are complex and expensive to evaluate.
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275
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65
Language
C++
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Last pushed
Feb 04, 2020
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