PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python
Copyright policy )PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python
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.
Reference work for the PEPit package (available at https://github.com/bgoujaud/PEPit)
Optimization, FOS: Computer and information sciences, Convex programming, Computer Science - Machine Learning, worst-case analyses, convergence analyses, splitting methods, Semidefinite programming, Machine Learning (cs.LG), Numerical mathematical programming methods, First-order methods, performance estimation problems, FOS: Mathematics, Convergence analyses, Semidefinite programming, Mathematics - Numerical Analysis, Performance estimation problems, Mathematics - Optimization and Control, Semidefinite programming., first-order methods, 000, Software, source code, etc. for problems pertaining to operations research and mathematical programming, Splitting methods, [MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC], Numerical Analysis (math.NA), semidefinite programming, 004, Optimization and Control (math.OC), Computer Science - Mathematical Software, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], optimization, Mathematical Software (cs.MS), Worst-case analyses
Optimization, FOS: Computer and information sciences, Convex programming, Computer Science - Machine Learning, worst-case analyses, convergence analyses, splitting methods, Semidefinite programming, Machine Learning (cs.LG), Numerical mathematical programming methods, First-order methods, performance estimation problems, FOS: Mathematics, Convergence analyses, Semidefinite programming, Mathematics - Numerical Analysis, Performance estimation problems, Mathematics - Optimization and Control, Semidefinite programming., first-order methods, 000, Software, source code, etc. for problems pertaining to operations research and mathematical programming, Splitting methods, [MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC], Numerical Analysis (math.NA), semidefinite programming, 004, Optimization and Control (math.OC), Computer Science - Mathematical Software, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], optimization, Mathematical Software (cs.MS), Worst-case analyses
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