Randomized Algorithms for Distributed Nonlinear Optimization Under Sparsity Constraints

Distributed optimization in multi-agent systems under sparsity constraints has recently received a lot of attention. In this paper, we consider the in-network minimization of a continuously differentiable nonlinear function which is a combination of local agent objective functions subject to sparsit...

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Bibliographic Details
Published in:	IEEE transactions on signal processing Vol. 64; no. 6; pp. 1420 - 1434
Main Authors:	Ravazzi, Chiara, Fosson, Sophie M., Magli, Enrico
Format:	Journal Article
Language:	English
Published:	New York IEEE 15.03.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Compressed sensing Convergence Distributed optimization Heuristic algorithms Links Mathematical analysis Mathematical models Monitoring Multi-agent systems Networks nonlinear optimization Nonlinearity Optimization randomized algorithms Signal processing algorithms sparse signal recovery Sparsity
ISSN:	1053-587X, 1941-0476
Online Access:	Get full text
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