论文标题
PEPIT：Python中一阶优化方法的计算机辅助最坏情况分析
PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python
论文作者
论文摘要
PEPIT是一个Python软件包，旨在简化对可能涉及梯度，投影，近端或线性优化的Oracles的大型一阶优化方法的最坏情况分析的访问，以及它们的大约或Bregman变体。简而言之，PEPIT是一个软件包，可以对一阶优化方法进行计算机辅助的最坏情况分析。关键的基本思想是提出执行最坏情况分析的问题，通常称为绩效估计问题（PEP），作为半决赛程序（SDP），可以通过数值解决。为此，包装用户只需要按照实施它们来编写一阶方法。然后，该软件包要照顾SDP建模零件，而最坏情况分析是通过标准求解器进行数值执行的。
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.
