Robustness of Accelerated First-Order Algorithms for Strongly Convex Optimization Problems

We study the robustness of accelerated first-order algorithms to stochastic uncertainties in gradient evaluation. Specifically, for unconstrained, smooth, strongly convex optimization problems, we examine the mean-squared error in the optimization variable when the iterates are perturbed by additive...

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Vydáno v:	IEEE transactions on automatic control Ročník 66; číslo 6; s. 2480 - 2495
Hlavní autoři:	Mohammadi, Hesameddin, Razaviyayn, Meisam, Jovanovic, Mihailo R.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.06.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accelerated first-order algorithms Acceleration Algorithms Computational geometry consensus networks control for optimization Convergence Convex analysis convex optimization Convexity Eigenvalues Evaluation First order algorithms Heuristic algorithms integral quadratic constraints linear matrix inequalities (LMIs) Mathematical analysis noise amplification Noise measurement Optimization Robustness second-order moments semidefinite programming Topology Uncertainty Upper bound Upper bounds White noise
ISSN:	0018-9286, 1558-2523
On-line přístup:	Získat plný text
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