Successive Lagrangian relaxation algorithm for nonconvex quadratic optimization
Successive Lagrangian relaxation algorithm for nonconvex quadratic optimization
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New York: Springer US
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English
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New York: Springer US
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Optimization problems whose objective function and constraints are quadratic polynomials are called quadratically constrained quadratic programs (QCQPs). QCQPs are NP-hard in general and are important in optimization theory and practice. There have been many studies on solving QCQPs approximately. Among them, a semidefinite program (SDP) relaxation...
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Full title
Successive Lagrangian relaxation algorithm for nonconvex quadratic optimization
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TN_cdi_proquest_journals_2002590763
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2002590763
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ISSN
0925-5001
E-ISSN
1573-2916
DOI
10.1007/s10898-018-0617-2
