Journal article
Solving Two-Trust-Region Subproblems Using Semidefinite Optimization with Eigenvector Branching
Journal of optimization theory and applications, Vol.202(1), pp.303-319
07/2024
DOI: 10.1007/s10957-022-02064-5
Abstract
Semidefinite programming (SDP) problems typically utilize a constraint of the form X >= xxT to obtain a convex relaxation of the condition X = xx(T), where x is an element of R-n. In this paper, we consider a new hyperplane branching method for SDP based on using an eigenvector of X - xx(T). This branching technique is related to previous work of Saxeena et al. (Math Prog Ser B 124:383-411, 2010, https:// doi.org/10.1007/s10107-0100371-9) who used such an eigenvector to derive a disjunctive cut. We obtain excellent computational results applying the new branching technique to difficult instances of the two-trust-region subproblem.
Details
- Title: Subtitle
- Solving Two-Trust-Region Subproblems Using Semidefinite Optimization with Eigenvector Branching
- Creators
- Kurt M. Anstreicher - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of optimization theory and applications, Vol.202(1), pp.303-319
- DOI
- 10.1007/s10957-022-02064-5
- ISSN
- 0022-3239
- eISSN
- 1573-2878
- Publisher
- Springer Nature
- Number of pages
- 17
- Language
- English
- Electronic publication date
- 06/30/2022
- Date published
- 07/2024
- Academic Unit
- Industrial and Systems Engineering; Computer Science; Business Analytics
- Record Identifier
- 9984380419102771
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