ISBN: 9783848482627 - iterative methods for nonlinear ill-posed problems: numerical functional analysis (3 risultati)

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  • Lingua: Inglese

    Editore: LAP Lambert Academic Publishing, 2012

    3848482622 / 9783848482627

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    Da: preigu, Osnabrück, Germaniapreigu

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    EUR 43,40

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    Taschenbuch. Condizione: Neu. Iterative Methods for Nonlinear ILL-posed Problems | Numerical Functional Analysis | Atef Ibrahim Elmahdy (u. a.) | Taschenbuch | Englisch | LAP Lambert Academic Publishing | EAN 9783848482627 | Verantwortliche Person für die EU: LAP Lambert Academic Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. …

  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing, 2012

    3848482622 / 9783848482627

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    Da: Mispah books, Redhill, SURRE, Regno UnitoMispah books

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    EUR 122,94

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    Paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

  • Lingua: Inglese

    Editore: LAP Lambert Academic Publishing, 2012

    3848482622 / 9783848482627

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    • Print on Demand

    Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH

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    EUR 54,70

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    Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Many problems in science and engineering have their mathematical formulation as an operator equation of the form F(x) = y, where F is a linear or nonlinear operator between certain function spaces. In practice, such equations are solved approximately using numerical methods, as their exact solution may not be often possible or may not be worth looking for due to physical constraints. In such situation, it is desirable to know how the so-called approximate solution approximates the exact solution, and what would be the error involved in such procedures. The main focus of the book is on the study of stably solving nonlinear ill posed operator equations of the form F(x)=y, with monotone nonlinear operator F in an infinite dimensional real Hilbert space X, that is , F obeys the monotonicity property. It is assumed that the exact data y is unknown and usually only noisy data are available. Problems of this type arise in a number of applications. Since the solution does not depend continuously on the data, the ill-posed problem has to be regularized. We considered iterative methods which converge to the unique solution of the method of Lavrentiev regularization.…