Noise amplifiation of momentum-based optimization algorithms

We study momentum-based first-order optimization algorithms in which the iterations utilize information from the two previous steps and are subject to additive white noise. For strongly convex quadratic problems, we utilize Jury stability criterion to provide a novel geometric characterization of li...

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Veröffentlicht in:	Proceedings of the American Control Conference S. 849 - 854
Hauptverfasser:	Mohammadi, Hesameddin, Razaviyayn, Meisam, Jovanovic, Mihailo R.
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	American Automatic Control Council 31.05.2023
Schlagworte:	Additive white noise Convergence convergence rate convex optimization First-order methods heavy-ball method Nesterov's accelerated algorithm noise amplification Optimization performance tradeoffs settling time Stability criteria Steady-state
ISSN:	2378-5861
Online-Zugang:	Volltext
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