PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python
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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
- 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 633
- 2014Analysis and Design of Optimization Algorithms via Integral Quadratic Constraintscited by 105
- 2021Acceleration Methodscited by 48
- 2021Extragradient Method: O(1/K) Last-Iterate Convergence for Monotone Variational Inequalities and Connections With Cocoercivitycited by 39
- 2015Smooth Strongly Convex Interpolation and Exact Worst-case Performance of First-order Methodscited by 36
- 2017Analysis of Optimization Algorithms via Integral Quadratic Constraints: Nonstrongly Convex Problemscited by 19
- 2014Optimized first-order methods for smooth convex minimizationcited by 18
- 2016A Simple Practical Accelerated Method for Finite Sumscited by 18
- 2015Exact Worst-case Performance of First-order Methods for Composite Convex Optimizationcited by 16
- 2018Optimizing the Efficiency of First-Order Methods for Decreasing the Gradient of Smooth Convex Functionscited by 14
- 2018Operator Splitting Performance Estimation: Tight contraction factors and optimal parameter selectioncited by 13
- 2019Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functionscited by 12
9 more
- 2018Lyapunov Functions for First-Order Methods: Tight Automated Convergence Guaranteescited by 11
- 2019Analysis and Design of First-Order Distributed Optimization Algorithms over Time-Varying Graphscited by 7
- 2019A frequency-domain analysis of inexact gradient methodscited by 7
- 2021An optimal gradient method for smooth strongly convex minimizationcited by 7
- 2017Exact worst-case convergence rates of the proximal gradient method for composite convex minimizationcited by 5
- 2014Douglas-Rachford Splitting: Complexity Estimates and Accelerated Variantscited by 4
- 2021The exact worst-case convergence rate of the gradient method with fixed step lengths for L-smooth functionscited by 4
- 2018Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programscited by 3
- 2021Automated Worst-Case Performance Analysis of Decentralized Gradient Descentcited by 2
Built on by
Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 6; 2026: 2.
10 papers.
- 2025The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Trainingcited by 31
- 2022Tight Last-Iterate Convergence of the Extragradient and the Optimistic Gradient Descent-Ascent Algorithm for Constrained Monotone Variational Inequalitiescited by 18
- 2024Data-Driven Performance Guarantees for Classical and Learned Optimizerscited by 5
- 2025Optimized projection-free algorithms for online learning: construction and worst-case analysiscited by 3
- 2025Greedy Alignment Principle for Optimizer Selectioncited by 2
- 2025Tight analyses of first-order methods with error feedbackcited by 1
- 2025Empirical and computer-aided robustness analysis of long-step and accelerated methods in smooth convex optimizationcited by 0
- 2025Reinforcement learning for adaptive interior point methods in convex quadratic programmingcited by 0
- 2026Step-Size Stability in Stochastic Optimization: A Theoretical Perspectivecited by 0
- 2026A Tight Theory of Error Feedback Algorithms in Distributed Optimizationcited by 0
- 2026A Tight Theory of Error Feedback Algorithms in Distributed Optimization29 May 2026
- 2026Step-Size Stability in Stochastic Optimization: A Theoretical Perspective10 Feb 2026
- 2025Greedy Alignment Principle for Optimizer Selection6 Dec 2025
- 2025Reinforcement learning for adaptive interior point methods in convex quadratic programming9 Sep 2025
- 2025Empirical and computer-aided robustness analysis of long-step and accelerated methods in smooth convex optimization11 Jun 2025
- 2025Optimized projection-free algorithms for online learning: construction and worst-case analysis6 Jun 2025
- 2025Tight analyses of first-order methods with error feedback5 Jun 2025
- 2025The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training31 Jan 2025
- 2024Data-Driven Performance Guarantees for Classical and Learned Optimizers22 Apr 2024
- 2022Tight Last-Iterate Convergence of the Extragradient and the Optimistic Gradient Descent-Ascent Algorithm for Constrained Monotone Variational Inequalities20 Apr 2022