Robust accelerated gradient methods for smooth strongly convex functions

SIAM Journal on Optimization, 30(1), pp. 717–751, 2020.

Publisher (DOI)arXiv:1805.10579

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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