Comments on “Surrogate Gradient Algorithm for Lagrangian Relaxation”

This note presents not only a surrogate subgradient method, but also a framework of surrogate subgradient methods. Furthermore, the framework can be used not only for separable problems, but also for coupled subproblems. The note delineates such a framework and shows that the algorithm can converges...

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Bibliographic Details
Published in:Journal of optimization theory and applications Vol. 137; no. 3; pp. 691 - 697
Main Author: Chang, T. S.
Format: Journal Article
Language:English
Published: Boston Springer US 01.06.2008
Springer
Springer Nature B.V
Subjects:
Algorithms
Applications of Mathematics
Applied sciences
Calculus of Variations and Optimal Control; Optimization
Engineering
Exact sciences and technology
Integer programming
Lagrange multiplier
Mathematical programming
Mathematics
Mathematics and Statistics
Operational research and scientific management
Operational research. Management science
Operations Research/Decision Theory
Optimization
Optimization techniques
Studies
Technical Note
Theory of Computation
Surrogate subgradient method
Nondifferentiable optimization
Lagrangian relaxation
Subgradient method
Lagrange multiplier
Subgradient optimization
Non differentiable programming
Relaxation method
Lagrangian method
Search algorithm
Lagrangian relaxation, Surrogate subgradient method
ISSN:0022-3239, 1573-2878
Online Access:Get full text
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Description
Summary:This note presents not only a surrogate subgradient method, but also a framework of surrogate subgradient methods. Furthermore, the framework can be used not only for separable problems, but also for coupled subproblems. The note delineates such a framework and shows that the algorithm can converges for a larger stepsize.
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ISSN:0022-3239
1573-2878
DOI:10.1007/s10957-007-9349-z