40 resultados para Semigroups of linear operators

em Bulgarian Digital Mathematics Library at IMI-BAS


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We consider a finite state automata based method of solving a system of linear Diophantine equations with coefficients from the set {-1,0,1} and solutions in {0,1}.

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The paper describes a learning-oriented interactive method for solving linear mixed integer problems of multicriteria optimization. The method increases the possibilities of the decision maker (DM) to describe his/her local preferences and at the same time it overcomes some computational difficulties, especially in problems of large dimension. The method is realized in an experimental decision support system for finding the solution of linear mixed integer multicriteria optimization problems.

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The paper describes a classification-based learning-oriented interactive method for solving linear multicriteria optimization problems. The method allows the decision makers describe their preferences with greater flexibility, accuracy and reliability. The method is realized in an experimental software system supporting the solution of multicriteria optimization problems.

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Sufficient conditions for the existence of Lp(k)-solutions of linear nonhomogeneous impulsive differential equations with unbounded linear operator are found. An example of the theory of the linear nonhomogeneous partial impulsive differential equations of parabolic type is given.

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A modification of the Nekrassov method for finding a solution of a linear system of algebraic equations is given and a numerical example is shown.

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2000 Mathematics Subject Classification: Primary 47A48, Secondary 60G12

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2010 Mathematics Subject Classification: 97D40, 97M10, 97M40, 97N60, 97N80, 97R80

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2010 Mathematics Subject Classification: 35Q15, 31A25, 37K10, 35Q58.

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2000 Mathematics Subject Classification: 62H15, 62P10.

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In this paper we propose a refinement of some successive overrelaxation methods based on the reverse Gauss–Seidel method for solving a system of linear equations Ax = b by the decomposition A = Tm − Em − Fm, where Tm is a banded matrix of bandwidth 2m + 1. We study the convergence of the methods and give software implementation of algorithms in Mathematica package with numerical examples. ACM Computing Classification System (1998): G.1.3.

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2000 Mathematics Subject Classification: 35J70, 35P15.

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2000 Mathematics Subject Classification: 35P20, 35J10, 35Q40.

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Mathematics Subject Classification: Primary 47A60, 47D06.