Minimization of nonlinear functions with linear constraints

G. I. Zabinyako, E. A. Kotel'nikov

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

Abstract: In this paper, some aspects of numerical realization of algorithms from the software package for solving problems of minimization of nonlinear functions including non-smooth functions with allowance for the linear constraints set by sparse matrices are considered. Examples of the solution of test problems are presented.

Key words: nonlinear programming, reduced gradient, method of conjugate gradients, quasi-Newton method, subgradient method, basis, superbasis.

UDC: 519.853.32

Received: 23.01.2012
Revised: 31.05.2012

 English version: Numerical Analysis and Applications, 2013, 6:3, 197–209

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