mlsanityData to 7 Oct 2026

Primal-dual extrapolation methods for monotone inclusions under local Lipschitz continuity

arXiv 2206.00973 ↗First posted 2 Jun 2022math.OC cs.LG cs.NAcited by 1

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Abstract (arXiv)

In this paper we consider a class of monotone inclusion (MI) problems of finding a zero of the sum of two monotone operators, in which one operator is maximal monotone while the other is it locally Lipschitz continuous. We propose primal-dual extrapolation methods to solve them using a point and operator extrapolation technique, whose parameters are chosen by a backtracking line search scheme. The proposed methods enjoy an operation complexity of cal O(log ε-1) and cal O(ε-1log ε-1), measured by the number of fundamental operations consisting only of evaluations of one operator and resolvent of the other operator, for finding an varepsilon-residual solution of strongly and non-strongly MI problems, respectively. The latter complexity significantly improves the previously best operation complexity cal O(varepsilon-2). As a byproduct, complexity results of the primal-dual extrapolation methods are also obtained for finding an varepsilon-KKT or varepsilon-residual solution of convex conic optimization, conic constrained saddle point, and variational inequality problems under it local Lipschitz continuity. We provide preliminary numerical results to demonstrate the performance of the proposed methods.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 30 references that are in the map. First posted on arXiv 2 Jun 2022.

Builds on

12 of 30 references.

References · 12

  1. 2020Near-Optimal Algorithms for Minimax Optimizationcited by 119
  2. 2021Accelerated Algorithms for Smooth Convex-Concave Minimax Problems with O(1/k2) Rate on Squared Gradient Normcited by 71
  3. 2018Golden Ratio Algorithms for Variational Inequalitiescited by 38
  4. 2018A Forward-Backward Splitting Method for Monotone Inclusions Without Cocoercivitycited by 35
  5. 2020Simple and optimal methods for stochastic variational inequalities, I: operator extrapolationcited by 35
  6. 2022The First Optimal Algorithm for Smooth and Strongly-Convex-Strongly-Concave Minimax Optimizationcited by 20
  7. 2022From Halpern's Fixed-Point Iterations to Nesterov's Accelerated Interpretations for Root-Finding Problemscited by 18
  8. 2021On Accelerated Methods for Saddle-Point Problems with Composite Structurecited by 13
  9. 2021New First-Order Algorithms for Stochastic Variational Inequalitiescited by 8
  10. 2018Iteration complexity of first-order augmented Lagrangian methods for convex conic programmingcited by 7
  11. 2014An inertial forward-backward-forward primal-dual splitting algorithm for solving monotone inclusion problemscited by 3
  12. 2021A Unifying Framework of Accelerated First-Order Approach to Strongly Monotone Variational Inequalitiescited by 3

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