PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python
(2024) Mathematical Programming Computation — Vol. 16, n° 3, p. 337-367 (2024)
Files
s12532-024-00259-7.pdf
Open Access - Adobe PDF
- 675.17 KB
Details
- Authors
- Abstract
- PEPit is a python package aiming at simplifying the access to worst-case analyses of a large family of first-order optimization methods possibly involving gradient, projection, proximal, or linear optimization oracles, along with their approximate, or Bregman variants. In short, PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods. The key underlying idea is to cast the problem of performing a worst-case analysis, often referred to as a performance estimation problem (PEP), as a semidefinite program (SDP) which can be solved numerically. To do that, the package users are only required to write first-order methods nearly as they would have implemented them. The package then takes care of the SDP modeling parts, and the worst-case analysis is performed numerically via standard solvers.
- Affiliations
Citations
Goujaud, B., Moucer, C., Glineur, F., Hendrickx, J., Taylor, A. B., & Dieuleveut, A. (2024). PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python. Mathematical Programming Computation, 16(3), 337-367. https://doi.org/10.1007/s12532-024-00259-7 (Original work published 2024)