Academic Journal
Parallel and Distributed Methods for Constrained Nonconvex Optimization?Part I: Theory.
| Τίτλος: | Parallel and Distributed Methods for Constrained Nonconvex Optimization?Part I: Theory. |
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| Συγγραφείς: | Scutari, Gesualdo, Facchinei, Francisco, Lampariello, Lorenzo |
| Πηγή: | IEEE Transactions on Signal Processing; Apr2017, Vol. 65 Issue 8, p1929-1944, 16p |
| Θεματικοί όροι: | Distributed algorithms, Distributed computing, Distributed computing management, Distributed computing software, Distributed computing equipment |
| Περίληψη: | 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. [ABSTRACT FROM PUBLISHER] |
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| Βάση Δεδομένων: | Complementary Index |
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