Reducing quadratic programming problem to regression problem: Stepwise algorithm

Reducing quadratic programming problem to regression problem: Stepwise algorithm

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Article ID: iaor20061844
Country: Netherlands
Volume: 164
Issue: 1
Start Page Number: 79
End Page Number: 88
Publication Date: Jul 2005
Journal: European Journal of Operational Research
Authors: , ,
Keywords: statistics: regression
Abstract:

Quadratic programming is concerned with minimizing a convex quadratic function subject to linear inequality constraints. The variables are assumed to be nonnegative. The unique solution of quadratic programming (QP) problem (QPP) exists provided that a feasible region is non-empty (the QP has a feasible space). A method for searching for the solution to a QP is provided on the basis of statistical theory. It is shown that QPP can be reduced to an appropriately formulated least squares (LS) problem (LSP) with equality constraints and nonnegative variables. This approach allows us to obtain a simple algorithm to solve QPP. The applicability of the suggested method is illustrated with numerical examples.

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