29 resultados para Variational Convergence


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This paper develops the results announced in the Note [14]. Using an eigenvalue problem governed by a variational inequality, we try to unify the theory concerning the post-critical equilibrium state of a thin elastic plate subjected to unilateral conditions.

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* This work was completed while the author was visiting the University of Limoges. Support from the laboratoire “Analyse non-linéaire et Optimisation” is gratefully acknowledged.

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2000 Mathematics Subject Classification: 44A15, 44A35, 46E30

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Mathematics Subject Classification: 30B10, 30B30; 33C10, 33C20

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The paper presents an example of methodological approach to the development of variational thinking skills in teaching programming. Various ways in solving a given task are implemented for the purpose. One of the forms, through which the variational thinking is manifested, is related to trail practical actions. In the process of comprehension of the properties thus acquired, students are doing their own (correct or incorrect) conclusions for other, hidden properties and at the same time they discover possibilities for new ways of action and acquiring of new effects. The variability and the generalizing function of thinking are in a close interrelation, and their interaction to a great extend determines the dynamics of the cognitive activity of the student.

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In this work we give su±cient conditions for k-th approximations of the polynomial roots of f(x) when the Maehly{Aberth{Ehrlich, Werner-Borsch-Supan, Tanabe, Improved Borsch-Supan iteration methods fail on the next step. For these methods all non-attractive sets are found. This is a subsequent improvement of previously developed techniques and known facts. The users of these methods can use the results presented here for software implementation in Distributed Applications and Simulation Environ- ments. Numerical examples with graphics are shown.

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We present quasi-Monte Carlo analogs of Monte Carlo methods for some linear algebra problems: solving systems of linear equations, computing extreme eigenvalues, and matrix inversion. Reformulating the problems as solving integral equations with a special kernels and domains permits us to analyze the quasi-Monte Carlo methods with bounds from numerical integration. Standard Monte Carlo methods for integration provide a convergence rate of O(N^(−1/2)) using N samples. Quasi-Monte Carlo methods use quasirandom sequences with the resulting convergence rate for numerical integration as good as O((logN)^k)N^(−1)). We have shown theoretically and through numerical tests that the use of quasirandom sequences improves both the magnitude of the error and the convergence rate of the considered Monte Carlo methods. We also analyze the complexity of considered quasi-Monte Carlo algorithms and compare them to the complexity of the analogous Monte Carlo and deterministic algorithms.

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2000 Mathematics Subject Classification: 49J40, 49J35, 58E30, 47H05

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2000 Mathematics Subject Classification: 41A05.

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AMS subject classification: 65J15, 47H04, 90C30.

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AMS subject classification: 49N35,49N55,65Lxx.

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2000 Mathematics Subject Classification: 60G18, 60E07

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2000 Mathematics Subject Classification: Primary 40C99, 46B99.

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2000 Mathematics Subject Classification: 41A25, 41A36, 40G15.