NDLI: Lagrangian Relaxations on Networks by ε-Subgradient Methods

Content Provider	Springer Nature Link
Author	Mijangos, E.
Copyright Year	2011
Abstract	The efficiency of the network flow techniques can be exploited in the solution of nonlinearly constrained network flow problems by means of approximate subgradient methods. The idea is to solve the dual problem by using ε-subgradient methods, where the dual function is estimated by minimizing approximately a Lagrangian function, which relaxes the side constraints and is subject only to network constraints. In this paper, convergence results for some kind of ε-subgradient methods are put forward. Moreover, in order to evaluate the quality of the solution and the efficiency of these methods some of them have been implemented and their performances computationally compared with codes that are able to solve the proposed test problems.
Starting Page	51
Ending Page	74
Page Count	24
File Format	PDF
ISSN	00223239
Journal	Journal of Optimization Theory and Applications
Volume Number	152
Issue Number	1
e-ISSN	15732878
Language	English
Publisher	Springer US
Publisher Date	2011-06-30
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Nonlinear programming Lagrangian relaxation Approximate subgradient methods Network flows Calculus of Variations and Optimal Control; Optimization Theory of Computation Engineering Operations Research/Decision Theory Optimization Applications of Mathematics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Control and Optimization Management Science and Operations Research

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