62 resultados para Regular operators, basic elementary operators, Banach lattices
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MSC 2010: 54C35, 54C60.
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2000 Mathematics Subject Classification: 35L15, Secondary 35L30.
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In this survey article we discuss some recent results concerning strong spectral estimates for Ruelle transfer operators for contact flows on basic sets similar to these of Dolgopyat obtained in the case of Anosov flows with C1 stable and unstable foliations. Some applications of Dolgopyat's results and the more recent ones are also described.
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In his paper [1], Bates investigates the existence of nonlinear, but highly smooth, surjective operators between various classes of Banach spaces. Modifying his basic method, he obtains the following striking results.
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We study the continuity of pseudo-differential operators on Bessel potential spaces Hs|p (Rn ), and on the corresponding Besov spaces B^(s,q)p (R ^n). The modulus of continuity ω we use is assumed to satisfy j≥0, ∑ [ω(2−j )Ω(2j )]2 < ∞ where Ω is a suitable positive function.
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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 second author is supported by the Alexander-von-Humboldt Foundation. He is on leave from: Institute of Mathematics, Academia Sinica, Beijing 100080, People’s Republic of China.
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In this paper, a novel approach for character recognition has been presented with the help of genetic operators which have evolved from biological genetics and help us to achieve highly accurate results. A genetic algorithm approach has been described in which the biological haploid chromosomes have been implemented using a single row bit pattern of 315 values which have been operated upon by various genetic operators. A set of characters are taken as an initial population from which various new generations of characters are generated with the help of selection, crossover and mutation. Variations of population of characters are evolved from which the fittest solution is found by subjecting the various populations to a new fitness function developed. The methodology works and reduces the dissimilarity coefficient found by the fitness function between the character to be recognized and members of the populations and on reaching threshold limit of the error found from dissimilarity, it recognizes the character. As the new population is being generated from the older population, traits are passed on from one generation to another. We present a methodology with the help of which we are able to achieve highly efficient character recognition.
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Mathematics Subject Classification: 26A33, 31C25, 35S99, 47D07.
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Mathematics Subject Classification: 26A33, 33E12, 33C20.
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Mathematics Subject Classification: 47B38, 31B10, 42B20, 42B15.
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2000 Mathematics Subject Classification: Primary 42B20; Secondary 42B15, 42B25
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2000 Mathematics Subject Classification: 42B10, 43A32.
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2000 Mathematics Subject Classification: Primary 26A33, 30C45; Secondary 33A35
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2000 Mathematics Subject Classification: Primary 30C45, Secondary 26A33, 30C80