mlsanityData to 7 Oct 2026

PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python

arXiv 2201.04040 ↗First posted 11 Jan 2022math.OC cs.LG cs.MScited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 21 of its 39 references that are in the map. First posted on arXiv 11 Jan 2022.

Builds on

21 of 39 references.

References · 21

  1. 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 633
  2. 2014Analysis and Design of Optimization Algorithms via Integral Quadratic Constraintscited by 105
  3. 2021Acceleration Methodscited by 48
  4. 2021Extragradient Method: O(1/K) Last-Iterate Convergence for Monotone Variational Inequalities and Connections With Cocoercivitycited by 39
  5. 2015Smooth Strongly Convex Interpolation and Exact Worst-case Performance of First-order Methodscited by 36
  6. 2017Analysis of Optimization Algorithms via Integral Quadratic Constraints: Nonstrongly Convex Problemscited by 19
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  10. 2018Optimizing the Efficiency of First-Order Methods for Decreasing the Gradient of Smooth Convex Functionscited by 14
  11. 2018Operator Splitting Performance Estimation: Tight contraction factors and optimal parameter selectioncited by 13
  12. 2019Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functionscited by 12
9 more
  1. 2018Lyapunov Functions for First-Order Methods: Tight Automated Convergence Guaranteescited by 11
  2. 2019Analysis and Design of First-Order Distributed Optimization Algorithms over Time-Varying Graphscited by 7
  3. 2019A frequency-domain analysis of inexact gradient methodscited by 7
  4. 2021An optimal gradient method for smooth strongly convex minimizationcited by 7
  5. 2017Exact worst-case convergence rates of the proximal gradient method for composite convex minimizationcited by 5
  6. 2014Douglas-Rachford Splitting: Complexity Estimates and Accelerated Variantscited by 4
  7. 2021The exact worst-case convergence rate of the gradient method with fixed step lengths for L-smooth functionscited by 4
  8. 2018Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programscited by 3
  9. 2021Automated Worst-Case Performance Analysis of Decentralized Gradient Descentcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 6; 2026: 2.

10 papers.