PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866913392757833728 |
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| author | Goujaud, Baptiste Moucer, Céline Glineur, François Hendrickx, Julien Taylor, Adrien Dieuleveut, Aymeric |
| author_facet | Goujaud, Baptiste Moucer, Céline Glineur, François Hendrickx, Julien Taylor, Adrien Dieuleveut, Aymeric |
| contents | 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 a standard solver. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2201_04040 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python Goujaud, Baptiste Moucer, Céline Glineur, François Hendrickx, Julien Taylor, Adrien Dieuleveut, Aymeric Optimization and Control Machine Learning Mathematical Software Numerical Analysis 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 a standard solver. |
| title | PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python |
| topic | Optimization and Control Machine Learning Mathematical Software Numerical Analysis |
| url | https://arxiv.org/abs/2201.04040 |