Description

Book Synopsis
This self-contained textbook covers the fundamentals of two basic topics of linear functional analysis: locally convex spaces and harmonic analysis. Readers will find detailed introductions to topological vector spaces, distribution theory, weak topologies, Fourier transform, Hilbert transform, and Calderon-Zygmund singular integrals. An ideal introduction to more advanced texts, the book complements Ciarlet's Linear and Nonlinear Functional Analysis with Applications (SIAM), where the titular topics were not treated. Pedagogical features such as detailed theorems and 93 problems make the book ideal for a one-semester first-year graduate course or for self-study.

The book is intended for advanced undergraduates and first-year graduate students and researchers. It is appropriate for the following courses: Advanced Numerical Analysis, Special Topics on Numerical Analysis, Topics on Data Science, Topics on Numerical Optimization, and Topics on Approximation Theory

Locally Convex Spaces and Harmonic Analysis: An

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A Paperback / softback by Philippe G. Ciarlet

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    View other formats and editions of Locally Convex Spaces and Harmonic Analysis: An by Philippe G. Ciarlet

    Publisher: Society for Industrial & Applied Mathematics,U.S.
    Publication Date: 30/12/2021
    ISBN13: 9781611976649, 978-1611976649
    ISBN10: 1611976642

    Description

    Book Synopsis
    This self-contained textbook covers the fundamentals of two basic topics of linear functional analysis: locally convex spaces and harmonic analysis. Readers will find detailed introductions to topological vector spaces, distribution theory, weak topologies, Fourier transform, Hilbert transform, and Calderon-Zygmund singular integrals. An ideal introduction to more advanced texts, the book complements Ciarlet's Linear and Nonlinear Functional Analysis with Applications (SIAM), where the titular topics were not treated. Pedagogical features such as detailed theorems and 93 problems make the book ideal for a one-semester first-year graduate course or for self-study.

    The book is intended for advanced undergraduates and first-year graduate students and researchers. It is appropriate for the following courses: Advanced Numerical Analysis, Special Topics on Numerical Analysis, Topics on Data Science, Topics on Numerical Optimization, and Topics on Approximation Theory

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