Abstract
Semidefinite relaxations of quadratic 0–1 programming or graph partitioning problems are well known to be of high quality. How- ever, solving them by primal-dual interior point methods can take much time even for problems of moderate size. Recently we proposed a spectral bundle method that allows to compute, within reasonable time, ap- proximate solutions to structured, large equality constrained semidefinite programs if the trace of the primal matrix variable is fixed. The latter property holds for the aforementioned applications. We extend the spec- tral bundle method so that it can handle inequality constraints without seriously increasing computation time. This makes it possible to apply cutting plane algorithms to semidefinite relaxations of real world sized instances. We illustrate the efficacy of the approach by giving some pre- liminary computational results.KeywordsInequality ConstraintBundle MethodSemidefinite RelaxationGraph Partitioning ProblemNull StepThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
