OSCARS for derivative-free stochastic direct search
Nobody has claimed this yet.
Assessment
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Newbie friendliness
- 72/100
- Issue type
- Documentation
- Clarity
- Mostly clear
- Activity status
- Active
- Tech stack
- r
- Domain
- documentation
Research direction
Start with the task view's "Global and Stochastic Optimization" section and inspect how existing packages are listed. Review the OSCARS examples, which are reported to run, and verify the package description before adding OSCARS to that section. Done means the package is represented consistently in the Optimization task view.
Written by the indexing model from the issue text.
Description
There is a new package "OSCARS: Global Bounded Optimization by the OSCARS-II Algorithm" based on variants of the One Side Cut Accelerated Random Search (OSCARS-II) algorithm by C.J.Price et al. It performs black-box optimization of general functions subject to simple bounds and implements a derivative-free stochastic direct search method.
I think this is interesting and would fit into the section on "Global and Stochastic Optimization".
"The OSCARS package provides the One Side Cut Accelerated Random Search
(OSCARS-II) algorithm. It performs black-box optimization of general
functions subject to simple bounds and implements a derivative-free
stochastic direct search method."
I have not performed any tests yet. The hint to the OSCARS-II algorithm provides some credibility, and the examples do run.
- Dominant language
- No language data
- Stars
- 6
- Forks
- 6
- Avg merge
- 7d 7h
- Merged PRs (30d)
- 3
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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