Journal
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume 62, Issue 3, Pages 641-656Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2013.2293126
Keywords
Nonconvex multi-agent problems; parallel and distributed optimization; successive convex approximation
Categories
Funding
- NSF [CNS-1218717, CMMI 0969600]
- NSF CAREER [ECCS-1254739]
- Hong Kong RGC [617810]
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [1218717] Funding Source: National Science Foundation
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We propose a novel decomposition framework for the distributed optimization of general nonconvex sum-utility functions arising naturally in the system design of wireless multi-user interfering systems. Our main contributions are i) the development of the first class of (inexact) Jacobi best-response algorithms with provable convergence, where all the users simultaneously and iteratively solve a suitably convexified version of the original sum-utility optimization problem; ii) the derivation of a general dynamic pricing mechanism that provides a unified view of existing pricing schemes that are based, instead, on heuristics; and iii) a framework that can be easily particularized to well-known applications, giving rise to very efficient practical (Jacobi or Gauss-Seidel) algorithms that outperform existing ad hoc methods proposed for very specific problems. Interestingly, our framework contains as special cases well-known gradient algorithms for nonconvex sum-utility problems, and many block-coordinate descent schemes for convex functions.
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