Robust Accelerated Primal-Dual Methods for Computing Saddle Points
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Publication:6130545
DOI10.1137/21m1462775arXiv2111.12743MaRDI QIDQ6130545
Xuan Zhang, Mert Gürbüzbalaban, Necdet Serhat Aybat
Publication date: 3 April 2024
Published in: SIAM Journal on Optimization (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2111.12743
Convex programming (90C25) Minimax problems in mathematical programming (90C47) Nonlinear programming (90C30) Stochastic programming (90C15) Stochastic approximation (62L20)
Cites Work
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- Primal-dual subgradient methods for convex problems
- Smooth minimization of non-smooth functions
- On the ergodic convergence rates of a first-order primal-dual algorithm
- Finite perturbation of convex programs
- Subgradient methods for saddle-point problems
- Accelerated schemes for a class of variational inequalities
- A first-order primal-dual algorithm for convex problems with applications to imaging
- On lower iteration complexity bounds for the convex concave saddle point problems
- Lower complexity bounds of first-order methods for convex-concave bilinear saddle-point problems
- Robust Stochastic Approximation Approach to Stochastic Programming
- First-Order Methods in Optimization
- Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization
- Optimization Methods for Large-Scale Machine Learning
- Prox-Method with Rate of Convergence O(1/t) for Variational Inequalities with Lipschitz Continuous Monotone Operators and Smooth Convex-Concave Saddle Point Problems
- A Primal-Dual Algorithm with Line Search for General Convex-Concave Saddle Point Problems
- Differentially Private Accelerated Optimization Algorithms
- Accelerated Stochastic Algorithms for Convex-Concave Saddle-Point Problems
- Simple and Optimal Methods for Stochastic Variational Inequalities, I: Operator Extrapolation
- Solving variational inequalities with Stochastic Mirror-Prox algorithm
- Excessive Gap Technique in Nonsmooth Convex Minimization
- Robust Accelerated Gradient Methods for Smooth Strongly Convex Functions
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