Accelerated methods for composite non-bilinear saddle point problem
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Abstract (arXiv)
Based on G. Lan's accelerated gradient sliding and general relation between the smoothness and strong convexity parameters of function under Legendre transformation we show that under rather general conditions the best known bounds for bilinear convex-concave smooth composite saddle point problem keep true for or non-bilinear convex-concave smooth composite saddle point problem. Moreover, we describe situations when the bounds differ and explain the nature of the difference.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jun 2019.
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Citing papers per year: 2021: 3; 2022: 3.
6 papers.
- 2021Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimizationcited by 34
- 2022The First Optimal Algorithm for Smooth and Strongly-Convex-Strongly-Concave Minimax Optimizationcited by 20
- 2021Convex-Concave Min-Max Stackelberg Gamescited by 9
- 2022Sion's Minimax Theorem in Geodesic Metric Spaces and a Riemannian Extragradient Algorithmcited by 9
- 2021Minimax Optimization: The Case of Convex-Submodularcited by 5
- 2022Robust No-Regret Learning in Min-Max Stackelberg Gamescited by 5
- 2022The First Optimal Algorithm for Smooth and Strongly-Convex-Strongly-Concave Minimax Optimization11 May 2022
- 2022Robust No-Regret Learning in Min-Max Stackelberg Games26 Mar 2022
- 2022Sion's Minimax Theorem in Geodesic Metric Spaces and a Riemannian Extragradient Algorithm13 Feb 2022
- 2021Minimax Optimization: The Case of Convex-Submodular1 Nov 2021
- 2021Convex-Concave Min-Max Stackelberg Games5 Oct 2021
- 2021Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization18 Apr 2021