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Decision brief
Rembo is a Matlab-based tool for high-dimensional Bayesian optimization using random embedding.
Good fit when
- When working with high-dimensional data spaces that require efficient exploration and optimization, making it ideal for problems exceeding typical dimensions
- If you are already invested in the Matlab environment and seek to leverage existing functions like 'fminsearch' for optimization tasks
Avoid when
- For low-dimensional spaces where full Bayesian optimization methods would be more efficient and less complex than random embedding
- In scenarios requiring open-source or licensed software when Rembo's license status is unknown, potentially limiting its use in certain projects
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (4747d since push)
- As of 2w
- Provenance
- Not a fork · Personal account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/ziyuw/remboSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A Matlab package for conducting Bayesian optimization through random embedding for high-dimensional spaces, including examples and data files for testing the functionality.
Capability facts
- Languages
- matlab
Source: github.language · Aug 4, 2026
Categories
Tags
README
INSTALL
The package is written in Matlab. Thus to install, just run startup.m. This package could make use of the function "fminsearch" from Matlab. This, however, is not necessary.
To run the lpsolve example from the paper, please download the data files from: http://www.cs.ubc.ca/~ziyuw/AC_blackbox_eval.tar. And untar the data files to the folder /demos/lpsolve/.
For agents
This page has a .md twin and JSON over the API.