Multi-Standard Quadratic Optimization: interior point methods and cone programming reformulation

Authors:
Immanuel M. Bomze;Werner Schachinger
Affiliations:
Dept. of Statistics and Decision Support Systems, University of Vienna, Vienna, Austria 1210;Dept. of Statistics and Decision Support Systems, University of Vienna, Vienna, Austria 1210
Venue:
Computational Optimization and Applications
Year:
2010

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Abstract

A Standard Quadratic Optimization Problem (StQP) consists of maximizing a (possibly indefinite) quadratic form over the standard simplex. Likewise, in a multi-StQP we have to maximize a (possibly indefinite) quadratic form over the Cartesian product of several standard simplices (of possibly different dimensions). Among many other applications, multi-StQPs occur in Machine Learning Problems. Several converging monotone interior point methods are established, which differ from the usual ones used in cone programming. Further, we prove an exact cone programming reformulation for establishing rigorous yet affordable bounds and finding improving directions.