Quadratic Convex Reformulations for Integer and Mixed-Integer Quadratic Programs

Baiyi Wu () and Rujun Jiang ()
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Baiyi Wu: Guangdong University of Foreign Studies
Rujun Jiang: The Chinese University of Hong Kong

Chapter Chapter 4 in Optimization and Control for Systems in the Big-Data Era, 2017, pp 43-58 from Springer

Abstract: Abstract We review recent advances in the quadratic convex reformulation (QCR) approach that is employed to derive efficient equivalent reformulations for mixed-integer quadratically constrained quadratic programming (MIQCQP) problems. Although MIQCQP problems can be directly plugged into and solved by standard MIQP solvers that are based on branch-and-bound algorithms, it is not efficient because the continuous relaxation of the standard MIQCQP reformulation is very loose. The QCR approach is a systematic way to derive tight equivalent reformulations. We will explore the QCR technique on subclasses of MIQCQP problems with simpler structures first and then generalize it step by step such that it can be applied to general MIQCQP problems. We also cover the recent extension of QCR on semi-continuous quadratic programming problems.

Keywords: Quadratic programming; Quadratic convex reformulation; Recent advances; Semi-continuous quadratic programming (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-319-53518-0_4

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DOI: 10.1007/978-3-319-53518-0_4

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