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  • Numerical Analysis: A Graduate Course

    Numerical Analysis: A Graduate Course by Stewart, David E.;

    Series: CMS/CAIMS Books in Mathematics; 4;

      • GET 12% OFF

      • Publisher's listprice EUR 80.24
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        33 279 Ft (31 694 Ft + 5% VAT)
      • Discount 12% (cc. 3 993 Ft off)
      • Discounted price 29 285 Ft (27 891 Ft + 5% VAT)

    29 285 Ft

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    printed on demand

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    Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.

    Product details:

    • Edition number 1st ed. 2022
    • Publisher Springer International Publishing
    • Date of Publication 2 December 2022
    • Number of Volumes 1 pieces, Book

    • ISBN 9783031081200
    • Binding Hardback
    • No. of pages632 pages
    • Size 235x155 mm
    • Weight 1245 g
    • Language English
    • Illustrations XV, 632 p. 114 illus., 66 illus. in color. Illustrations, black & white
    • 267

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    Long description:

    This book aims to introduce graduate students to the many applications of numerical computation, explaining in detail both how and why the included methods work in practice. The text addresses numerical analysis as a middle ground between practice and theory, addressing both the abstract mathematical analysis and applied computation and programming models instrumental to the field. While the text uses pseudocode, Matlab and Julia codes are available online for students to use, and to demonstrate implementation techniques. The textbook also emphasizes multivariate problems alongside single-variable problems and deals with topics in randomness, including stochastic differential equations and randomized algorithms, and topics in optimization and approximation relevant to machine learning. Ultimately, it seeks to clarify issues in numerical analysis in the context of applications, and presenting accessible methods to students in mathematics and data science.

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    Table of Contents:

    Basics of mathematical computation.- Computing with Matrices and Vectors.- Solving nonlinear equations.- Approximations and interpolation.- Integration and differentiation.- Differential equations.- Randomness.- Optimization.- Appendix A: What you need from analysis.

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