Subgradient methods in network resource allocation: Rate analysis

Angelia Nedić; Asuman Ozdaglar

doi:10.1109/CISS.2008.4558699

Subgradient methods in network resource allocation: Rate analysis

Angelia Nedić, Asuman Ozdaglar

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

31 Scopus citations

Abstract

We consider dual subgradient methods for solving (nonsmooth) convex constrained optimization problems. Our focus is on generating approximate primal solutions with performance guarantees and providing convergence rate analysis. We propose and analyze methods that use averaging schemes to generate approximate primal optimal solutions. We provide estimates on the convergence rate of the generated primal solutions in terms of both the amount of feasibility violation and bounds on the primal function values. The feasibility violation and primal value estimates are given per iteration, thus providing practical stopping criteria. We provide a numerical example that illustrate the performance of the subgradient methods with averaging in a network resource allocation problem.

Original language	English (US)
Title of host publication	CISS 2008, The 42nd Annual Conference on Information Sciences and Systems
Pages	1189-1194
Number of pages	6
DOIs	https://doi.org/10.1109/CISS.2008.4558699
State	Published - 2008
Externally published	Yes
Event	CISS 2008, 42nd Annual Conference on Information Sciences and Systems - Princeton, NJ, United States Duration: Mar 19 2008 → Mar 21 2008

Publication series

Name	CISS 2008, The 42nd Annual Conference on Information Sciences and Systems

Other

Other	CISS 2008, 42nd Annual Conference on Information Sciences and Systems
Country/Territory	United States
City	Princeton, NJ
Period	3/19/08 → 3/21/08

ASJC Scopus subject areas

Computer Science Applications
Information Systems
Control and Systems Engineering

Access to Document

10.1109/CISS.2008.4558699

Cite this

Subgradient methods in network resource allocation: Rate analysis. / Nedić, Angelia; Ozdaglar, Asuman.
CISS 2008, The 42nd Annual Conference on Information Sciences and Systems. 2008. p. 1189-1194 4558699 (CISS 2008, The 42nd Annual Conference on Information Sciences and Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nedić, A & Ozdaglar, A 2008, Subgradient methods in network resource allocation: Rate analysis. in CISS 2008, The 42nd Annual Conference on Information Sciences and Systems., 4558699, CISS 2008, The 42nd Annual Conference on Information Sciences and Systems, pp. 1189-1194, CISS 2008, 42nd Annual Conference on Information Sciences and Systems, Princeton, NJ, United States, 3/19/08. https://doi.org/10.1109/CISS.2008.4558699

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abstract = "We consider dual subgradient methods for solving (nonsmooth) convex constrained optimization problems. Our focus is on generating approximate primal solutions with performance guarantees and providing convergence rate analysis. We propose and analyze methods that use averaging schemes to generate approximate primal optimal solutions. We provide estimates on the convergence rate of the generated primal solutions in terms of both the amount of feasibility violation and bounds on the primal function values. The feasibility violation and primal value estimates are given per iteration, thus providing practical stopping criteria. We provide a numerical example that illustrate the performance of the subgradient methods with averaging in a network resource allocation problem.",

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AB - We consider dual subgradient methods for solving (nonsmooth) convex constrained optimization problems. Our focus is on generating approximate primal solutions with performance guarantees and providing convergence rate analysis. We propose and analyze methods that use averaging schemes to generate approximate primal optimal solutions. We provide estimates on the convergence rate of the generated primal solutions in terms of both the amount of feasibility violation and bounds on the primal function values. The feasibility violation and primal value estimates are given per iteration, thus providing practical stopping criteria. We provide a numerical example that illustrate the performance of the subgradient methods with averaging in a network resource allocation problem.

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Subgradient methods in network resource allocation: Rate analysis

Abstract

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