Aggregations of quadratic inequalities and hidden hyperplane convexity

SIAM JOURNAL ON OPTIMIZATION(2024)

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摘要
We study properties of the convex hull of a set S described by quadratic inequalities. A simple way of generating inequalities valid on S is to take nonnegative linear combinations of the defining inequalities of S. We call such inequalities aggregations. Special aggregations naturally contain the convex hull of S, and we give sufficient conditions for intersection of such aggregations to define the convex hull. We introduce the notion of hidden hyperplane convexity (HHC), which is related to the classical notion of hidden convexity of quadratic maps. We show that if the quadratic map associated with S satisfies HHC, then the convex hull of S is defined by special aggregations. To the best of our knowledge, this result generalizes all known results regarding aggregations defining convex hulls. Using this sufficient condition, we are able to recognize previously unknown classes of sets where aggregations lead to convex hull. We show that the condition known as the positive definite linear combination for every triple of inequalities, together with HHC, is sufficient for finitely many aggregations to define the convex hull, answering a question raised in [S. S. Dey, G. Munoz, and F. Serrano, On Obtaining the Convex Hull of Quadratic Inequalities via Aggregations, arXiv:2106.12629, 2021]. All the above results are for sets defined using open quadratic inequalities. For closed quadratic inequalities, we prove a new result regarding aggregations giving the convex hull, without topological assumptions on S, which were needed in [S. Modaresi and J. P. Vielma, Math. Program., 164 (2017), pp. 383--409; S. S. Dey, G. Munoz, and F. Serrano, On Obtaining the Convex Hull of Quadratic Inequalities via Aggregations, arXiv:2106.12629, 2021].
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关键词
quadratic programming,polynomial optimization,convex geometry
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