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About 10,000 papers for “Quadratic programming”

Quadratic programming

Berwin A. Turlach, Stephen J. Wright · Wiley Interdisciplinary Reviews Computational Statistics · 2015

Optimization problems in which a quadratic objective function is optimized subject to linear constraints on the parameters are known as quadratic programming problems ( QPs ). This focus article reviews algorithms for convex QPs (in which the objective is a convex function) and provides pointers to various online resources about QPs . WIREs Comput Stat 2015, 7:153–159. doi: 10.1002/wics.1344 This article is categorized under: Algorithms and Computational Methods > Quadratic and Nonlinear Programming

Cited by 10

Quadratic programming

Gray, Deal Orley · Rice Digital Scholarship Archive (Rice University) · 2016

Cited by 0Open access

Quadratic Programming

Robert J. Vanderbei · International series in management science/operations research/International series in operations research & management science · 2013

Cited by 1

Quadratic Programming

Reiner Horst, Pãnos M. Pardalos, Nguyễn Văn Thoại · Nonconvex optimization and its applications · 2000

Cited by 1

Quadratic Matrix Programming

Amir Beck · SIAM Journal on Optimization · 2007

We introduce and study a special class of nonconvex quadratic problems in which the objective and constraint functions have the form $f(\boldmath X)={Tr}(\boldmath X^T \boldmath A \boldmath X) + 2 Tr(\boldmath B^T \boldmath X) +c, \boldmath X \in {\real R}^{n \times r}$. The latter formulation is termed quadratic matrix programming (QMP) of order r. We construct a specially devised semidefinite relaxation (SDR) and dual for the QMP problem and show that under some mild conditions strong duality holds for QMP problems with at most r constraints. Using a result on the equivalence of two characterizations of the nonnegativity property of quadratic functions of the above form, we are able to compare the constructed SDR and dual problems to other known SDRs and dual formulations of the problem. An application to robust least squares problems is discussed.

Cited by 84

General Quadratic Programming

Claude‐Alain Burdet · Defense Technical Information Center (DTIC) · 1971

An algorithm is presented for the general (not necessarily convex or concave) quadratic programming problem over a linearly constrained set. The algorithm is finitely convergent and makes use of a convex quadratic programming method as a subroutine (like the quadratic simplex for instance). The basic tool for this method is a facial decomposition for polyhedral sets.

Cited by 1