Journal
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume 65, Issue 8, Pages 1929-1944Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2016.2637317
Keywords
Distributed algorithms; nonconvex optimization; successive convex approximation
Categories
Funding
- USA National Science Foundation [CIF 1632599, CIF 1564044, 1555850]
- Office of Naval Research [N00014-16-1-2244]
- MIUR project PLATINO [PON01_01007]
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1564044] Funding Source: National Science Foundation
- Directorate For Engineering
- Div Of Electrical, Commun & Cyber Sys [1555850] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1632599] Funding Source: National Science Foundation
Ask authors/readers for more resources
In this two-part paper, we propose a general algorithmic framework for the minimization of a nonconvex smooth function subject to nonconvex smooth constraints, and also consider extensions to some structured, nonsmooth problems. The algorithm solves a sequence of (separable) strongly convex problems and maintains feasibility at each iteration. Convergence to a stationary solution of the original nonconvex optimization is established. Our framework is very general and flexible and unifies several existing successive convex approximation (SCA)-based algorithms. More importantly, and differently from current SCA approaches, it naturally leads to distributed and parallelizable implementations for a large class of nonconvex problems. This Part I is devoted to the description of the framework in its generality. In Part II, we customize our general methods to several (multiagent) optimization problems in communications, networking, and machine learning; the result is a new class of centralized and distributed algorithms that compare favorably to existing ad-hoc (centralized) schemes.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available