Journal article
Two "well-known" properties of subgradient optimization
Mathematical programming, Vol.120(1), pp.213-220
08/01/2009
DOI: 10.1007/s10107-007-0148-y
Abstract
The subgradient method is both a heavily employed and widely studied algorithm for non-differentiable optimization. Nevertheless, there are some basic properties of subgradient optimization that, while "well known" to specialists, seem to be rather poorly known in the larger optimization community. This note concerns two such properties, both applicable to subgradient optimization using the divergent series steplength rule. The first involves convergence of the iterative process, and the second deals with the construction of primal estimates when subgradient optimization is applied to maximize the Lagrangian dual of a linear program. The two topics are related in that convergence of the iterates is required to prove correctness of the primal construction scheme.
Details
- Title: Subtitle
- Two "well-known" properties of subgradient optimization
- Creators
- Kurt M. Anstreicher - University of IowaLaurence A. Wolsey - UCLouvain
- Resource Type
- Journal article
- Publication Details
- Mathematical programming, Vol.120(1), pp.213-220
- Publisher
- Springer Nature
- DOI
- 10.1007/s10107-007-0148-y
- ISSN
- 0025-5610
- eISSN
- 1436-4646
- Number of pages
- 8
- Language
- English
- Date published
- 08/01/2009
- Academic Unit
- Industrial and Systems Engineering; Computer Science; Business Analytics
- Record Identifier
- 9984380524202771
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