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On non-convex quadratic programming with box constraints

Research output: Contribution to journalJournal article


<mark>Journal publication date</mark>2009
<mark>Journal</mark>SIAM Journal on Optimization
Number of pages17
<mark>Original language</mark>English


Nonconvex quadratic programming with box constraints is a fundamental NP-hard global optimization problem. Recently, some authors have studied a certain family of convex sets associated with this problem. We prove several fundamental results concerned with these convex sets: we determine their dimension, characterize their extreme points and vertices, show their invariance under certain affine transformations, and show that various linear inequalities induce facets. We also show that the sets are closely related to the Boolean quadric polytope, a fundamental polytope in the field of polyhedral combinatorics. Finally, we give a classification of valid inequalities and show that this yields a finite recursive procedure to check the validity of any proposed inequality.

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