962 resultados para Variational Principle


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∗ The final version of this paper was sent to the editor when the author was supported by an ARC Small Grant of Dr. E. Tarafdar.

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The generalized Wiener-Hopf equation and the approximation methods are used to propose a perturbed iterative method to compute the solutions of a general class of nonlinear variational inequalities.

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We prove some multiplicity results concerning quasilinear elliptic equations with natural growth conditions. Techniques of nonsmooth critical point theory are employed.

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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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One of the problems in AI tasks solving by neurocomputing methods is a considerable training time. This problem especially appears when it is needed to reach high quality in forecast reliability or pattern recognition. Some formalised ways for increasing of networks’ training speed without loosing of precision are proposed here. The offered approaches are based on the Sufficiency Principle, which is formal representation of the aim of a concrete task and conditions (limitations) of their solving [1]. This is development of the concept that includes the formal aims’ description to the context of such AI tasks as classification, pattern recognition, estimation etc.

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Mathematics Subject Classification: 35CXX, 26A33, 35S10

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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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MSC 2010: 30C45, 30C55

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MSC 2010: 30C45, 30C55

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MSC 2010: 26A33, 33E12, 35B45, 35B50, 35K99, 45K05 Dedicated to Professor Rudolf Gorenflo on the occasion of his 80th anniversary

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2000 Mathematics Subject Classification: 47H04, 65K10.

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2002 Mathematics Subject Classification: 35J15, 35J25, 35B05, 35B50

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2010 Mathematics Subject Classification: 94A17, 62B10, 62F03.