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New Lagrangian function for nonconvex primal-dual decomposition

โœ Scribed by A. Tanikawa; H. Mukai

Book ID
103930257
Publisher
Elsevier Science
Year
1987
Tongue
English
Weight
754 KB
Volume
13
Category
Article
ISSN
0898-1221

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โœฆ Synopsis

In this paper, a new Lagrangian function is reported which is particularly suited for large-scale nonconvex optimization problems with separable structure. Our modification convexities the standard Lagrangian function without destroying its separable structure so that the primal~lual decomposition technique can be applied even to nonconvex optimization problems. Furthermore, the proposed Lagrangian results in two levels of iterative optimization as compared with the three levels needed for techniques recently proposed for nonconvex primal-dual decomposition.

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New dual-type decomposition algorithm for nonconvex separable optimization problems
โœ P. Tatjewski ๐Ÿ“‚ Article ๐Ÿ“… 1989 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 776 KB

Although the augmented Lagrange function is not separable, suitable reformulation of this function and introduction of auxiliary variables lead to a decomposition algorithm with simple and efficient adjusting rules for upper level variables.