期刊
MATHEMATICAL PROGRAMMING
卷 151, 期 1, 页码 89-116出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s10107-015-0888-z
关键词
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This paper illustrates the fundamental connection between nonconvex quadratic optimization and copositive optimization-a connection that allows the reformulation of nonconvex quadratic problems as convex ones in a unified way. We focus on examples having just a few variables or a few constraints for which the quadratic problem can be formulated as a copositive-style problem, which itself can be recast in terms of linear, second-order-cone, and semidefinite optimization. A particular highlight is the role played by the geometry of the feasible set.
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