This article is cited in 1 paper

Applying a reduced gradient in the quadratic programming

E. A. Kotel'nikov

Institute of Computational Mathematics and Mathematical Geophysics (Computing Center), Siberian Branch of the Russian Academy of Sciences

Abstract: This paper considers specific aspects of implementing the algorithm for solving problems of the quadratic programming, which is based on the reduced gradient method. In the current subspace of the superbasis variables, minimization is carried out by the conjugate gradient method. Some examples of solution of test problems are given.

Key words: quadratic programming, reduced gradient, conjugate gradient, basis, superbasis.

UDC: 519.853.32

Received: 10.04.2009
Revised: 13.07.2009

 English version: Numerical Analysis and Applications, 2010, 3:1, 17–24

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