artofscience / artofscience/SAOR
Implement Bayesian global optimization (parallel multi starts)
- Dominant language
- Python
- Stars
- 5
- Forks
- 1
- PR merge metrics
- No merged PRs in 30d
Description
Its an easy way to exploit the cores laying around (which are doing nothing).
See the theory (in work) outlined in my https://github.com/dirkmunro89/SOAPs/tree/main/bayopt
I have a first implementation; I am testing it on the DTU MBB, right now.
Eventually I want to get in touch with a probability / stats person (at the TU maybe); in order to get some proper feedback on it.
Contributor guide
No contributing guide indexed for this repository
Research direction
The issue names no SAOR files, tests, or entry points. Start by reading the linked SOAPs bayopt theory and inspecting the repository for the current optimization implementation; done should mean parallel multi-start Bayesian optimization is implemented and validated against the DTU MBB case.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100