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Solving Large-Scale Sparse Semidefinite Programs for Combinatorial Optimization
Abstract
We present a dual-scaling interior-point algorithm and show how it exploits the structure and sparsity of some large-scale problems. We solve the positive semidefinite relaxation of combinatorial and quadratic optimization problems subject to boolean constraints. We report the first computational results of interior-point algorithms for approximating maximum cut semidefinite programs with dimension up to 3,000.
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Goemans, M. X., & Williamson, D. P. (1995) · Journal of the ACM · doi:10.1145/227683.227684 · Cited by 3,587
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Burer, S., & Monteiro, R. D. C. (2003) · Mathematical Programming · doi:10.1007/s10107-002-0352-8 · Cited by 770
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Boyd, S., Diaconis, P., & Xiao, L. (2004) · SIAM Review · doi:10.1137/s0036144503423264 · Cited by 744
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Metadata
- Authors
- Benson, S. J., Ye, Y., & Zhang, X.
- Venue
- SIAM Journal on Optimization
- Published
- 2000-01
- Volume
- 10
- Issue
- 2
- Pages
- 443-461
- ISSN
- 1052-6234 (ISSN-L)
- 1095-7189
- OpenAlex
- W2061607408
- Cited by
- 276
- References
- 1
- FWCI
- 21.12
- Type
- journal article
- Language
- en
- Open access
- closed
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