Publications · Journal article · SIAM J. Optim. 2020
Robust accelerated gradient methods for smooth strongly convex functions
SIAM Journal on Optimization, 30(1), pp. 717–751, 2020.
In brief
Analyzes accelerated gradient methods under inexact gradients through a robustness measure (asymptotic noise amplification), proves a Heisenberg-like trade-off — the product of speed and robustness is bounded below — and designs parameters on the resulting Pareto frontier, obtaining methods that retain acceleration while controlling noise amplification.
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@article{aybat2020robust,
title = {Robust accelerated gradient methods for smooth strongly convex functions},
author = {Necdet Serhat Aybat and Alireza Fallah and Mert Gürbüzbalaban and Asuman Ozdaglar},
year = {2020},
journal = {SIAM Journal on Optimization},
volume = {30(1)},
pages = {717--751},
doi = {10.1137/19M1244925},
}Relatedsame topics