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applying a reduced gradient in quadratic programming

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

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Authors and Affiliations

  1. Institute of Computational Mathematics and Mathematical Geophysics, Siberian Branch, Russian Academy of Sciences, pr. Akad. Lavrent’ eva 6, Novosibirsk, 630090, Russia

    E. A. Kotel’nikov

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  1. E. A. Kotel’nikov
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Original Russian Text © E.A. Kotel’nikov, 2010, published in Sibirskii Zhurnal Vychislitel’noi Matematiki, 2010, Vol. 13, No. 1, pp. 23–31.

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Kotel’nikov, E.A. applying a reduced gradient in quadratic programming. Numer. Analys. Appl. 3, 17–24 (2010). https://doi.org/10.1134/S1995423910010039

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  • DOI: https://doi.org/10.1134/S1995423910010039

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