Nevergrad
Derivative-free optimization methods and benchmark interfaces.
External resource. No execution or independent verification is claimed here.
Which derivative-free optimizers attain lower regret under an identical bounded evaluation budget?
Use a prespecified objective suite with known minima, fixed dimensions and seeds, equal evaluation counts, and full outcome distributions rather than best-run selection.
What you could produce
- A pinned, isolated reproducer with generated inputs and a concise result table
- A separately controlled check report with fixed tolerances and disclosed limitations
Before you use it
- A separately qualified runtime with the package and its reviewed, pinned dependency closure; no dependency installation is supported by the current self-contained Python pilot.
Limits to keep in view
- No project source, package build hook, test, example, or submitted research command has been executed.
- The documented pilot supports self-contained Python 3.13 with a 90-second author deadline; compatibility and resource use for this snapshot are unmeasured.
Source and permission context
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Catalog listing reviewed. This review covers the description and source links displayed here.
Approved for catalog metadata and links. The displayed entry identifies Nevergrad with the concise collection-authored summary “Derivative-free optimization methods and benchmark interfaces.” and points to the public upstream repository https://github.com/facebookresearch/nevergrad. The wording describes function and possible investigation without reproducing upstream source or documentation, claiming execution, or implying endorsement or rights beyond the recorded scopes.
Reviewed 2026-09-14. Copying or adapting source files remains subject to their own terms.
code · MIT
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