Nonconvex Quadratic Programming

Hoang Tuy
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Hoang Tuy: Institute of Mathematics

Chapter Chapter 10 in Convex Analysis and Global Optimization, 2016, pp 337-390 from Springer

Abstract: Abstract Nonconvex quadratic programming deals with optimization problems described by means of linear and quadratic functions, i.e., functions with lowest degree of nonconvexity. One of the earliest significant results in this area is the celebrated S-Lemma of Yakubovich which plays a major role in the development of quadratic optimization. In this chapter, a study of nonconvex quadratic programming is provided that starts with a generalized S-Lemma established on the basis of a special minimax theorem. In particular a thorough study, including most recent results, is presented of quadratic programming with single quadratic constraint and quadratic programming under linear constraints.

Keywords: Nonconvex Quadratic; Convex Minorant; Indefinite Quadratic; Rectangular Subdivision; Nonconvex Variational (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/978-3-319-31484-6_10

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