Nonlinear Perturbation-Based Non-Convex Optimization Over Time-Varying Networks

Decentralized optimization strategies are helpful for various applications, from networked estimation to distributed machine learning. This paper studies finite-sum minimization problems described over a network of nodes and proposes a computationally efficient algorithm that solves distributed conv...

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Veröffentlicht in:	IEEE transactions on network science and engineering Jg. 11; H. 6; S. 6461 - 6469
Hauptverfasser:	Doostmohammadian, Mohammadreza, Gabidullina, Zulfiya R., Rabiee, Hamid R.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.11.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Convergence Convexity Cost function Costs Data exchange Data transmission Distributed algorithm Linear programming locally non-convex optimization Machine learning Network analysis network science Network topology Nonlinear dynamics Nonlinearity Optimization Packet switching Perturbation perturbation theory Quantization (signal) Switches
ISSN:	2327-4697, 2334-329X
Online-Zugang:	Volltext
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