ENGLISH

A branch and cut algorithm for nonconvex quadratically constrained quadratic programming

By Charles Audet , Pierre Hansen , Brigitte Jaumard

Book information

Year
2000
Language
english
Format
PDF
Filesize
221 kB (225890 bytes)
Pages
22\22
Library
mexmat
Time added
2009-11-09 02:42:13

Description

We present a branch and cut algorithm that yields in finite time, a globally ☼-optimal solution (with respect to feasibility and optimality) of the nonconvex quadratically constrained quadratic programming problem. The idea is to estimate all quadratic terms by successive linearizations within a branching tree using Reformulation-Linearization Techniques (RLT). To do so, four classes of linearizations (cuts), depending on one to three parameters, are detailed. For each class, we show how to select the best member with respect to a precise criterion. The cuts introduced at any node of the tree are valid in the whole tree, and not only within the subtree rooted at that node. In order to enhance the computational speed, the structure created at any node of the tree is flexible enough to be used at other nodes. Computational results are reported that include standard test problems taken from the literature. Some of these problems are solved for the first time with a proof of global optimality.

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