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Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems

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5 Author(s)
Gesualdo Scutari ; Dept. of Electr. Eng., State Univ. of New York at Buffalo, Buffalo, NY, USA ; Francisco Facchinei ; Peiran Song ; Daniel P. Palomar
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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.

Published in:

IEEE Transactions on Signal Processing  (Volume:62 ,  Issue: 3 )