Publications · Journal article · SIAM J. Optim. 2024
Robust accelerated primal-dual methods for computing saddle points
SIAM Journal on Optimization, 34(1), pp. 1097–1130, 2024.
In brief
Accelerated primal–dual methods solve saddle-point problems quickly but, like momentum methods, amplify gradient errors. For strongly convex–strongly concave problems the paper quantifies that amplification as a robustness measure, computes it for the accelerated primal–dual family, and shows how to choose parameters on the speed–robustness frontier — the saddle-point counterpart of the trade-off for momentum methods.
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Three ways to reach a saddle point → — a playground built around the ideas in this paper.
Cite
@article{zhang2024rapd,
title = {Robust accelerated primal-dual methods for computing saddle points},
author = {Xuan Zhang and Necdet Serhat Aybat and Mert Gürbüzbalaban},
year = {2024},
journal = {SIAM Journal on Optimization},
volume = {34(1)},
pages = {1097--1130},
doi = {10.1137/21M1462775},
}Relatedsame topics