Journal article
Computational enhancements in low-rank semidefinite programming
Optimization methods & software, Vol.21(3), pp.493-512
06/01/2006
DOI: 10.1080/10556780500286582
Abstract
We discuss computational enhancements for the low-rank semidefinite programming algorithm, including the extension to block semidefinite programs (SDPs), an exact linesearch procedure, and a dynamic rank reduction scheme. A truncated-Newton method is also introduced, and several preconditioning strategies are proposed. Numerical experiments illustrating these enhancements are provided on a wide class of test problems. In particular, the truncated-Newton variant is able to achieve high accuracy in modest amounts of time on maximum-cut-type SDPs.
Details
- Title: Subtitle
- Computational enhancements in low-rank semidefinite programming
- Creators
- Samuel Burer - University of IowaChanghui Choi - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Optimization methods & software, Vol.21(3), pp.493-512
- Publisher
- Taylor & Francis
- DOI
- 10.1080/10556780500286582
- ISSN
- 1055-6788
- eISSN
- 1029-4937
- Language
- English
- Date published
- 06/01/2006
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380401502771
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