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PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python
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DOI：10.1007/s12532-024-00259-7.png)
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摘要
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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.
Keyword:
Optimization
Worst-case analyses
Convergence analyses
Performance estimation problems
First-order methods
Splitting methods
Semidefinite programming
期刊
IF:
3.6
论文数:
198
被引数:
1.9K
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