Recovery methodologies regularization sampling par freeden willi (17 résultats)

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  • Langue : anglais

    Edité par American Mathematical Society, 2023

    1470473453 / 9781470473457

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    Edité par American Mathematical Society, 2023

    1470473453 / 9781470473457

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  • Langue : anglais

    Edité par American Mathematical Society, 2023

    1470473453 / 9781470473457

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  • Langue : anglais

    Edité par American Mathematical Society, Providence, 2023

    1470473453 / 9781470473457

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    Paperback. Etat : new. Paperback. The goal of this book is to introduce the reader to methodologies in recovery problems for objects, such as functions and signals, from partial or indirect information. The recovery of objects from a set of data demands key solvers of inverse and sampling problems. Until recently, connections between the mathematical areas of inverse problems and sampling were rather tenuous. However, advances in several areas of mathematical research have revealed deep common threads between them, which proves that there is a serious need for a unifying description of the underlying mathematical ideas and concepts. Freeden and Nashed present an integrated approach to resolution methodologies from the perspective of both these areas.Researchers in sampling theory will benefit from learning about inverse problems and regularization methods, while specialists in inverse problems will gain a better understanding of the point of view of sampling concepts. This book requires some basic knowledge of functional analysis, Fourier theory, geometric number theory, constructive approximation, and special function theory. By avoiding extreme technicalities and elaborate proof techniques, it is an accessible resource for students and researchers not only from applied mathematics, but also from all branches of engineering and science. Introduces the reader to methodologies in recovery problems for objects, such as functions and signals, from partial or indirect information. By avoiding extreme technicalities and elaborate proof techniques, this is an accessible resource for students and researchers. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

  • Langue : anglais

    Edité par American Mathematical Society, US, 2023

    1470473453 / 9781470473457

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    Paperback. Etat : New. The goal of this book is to introduce the reader to methodologies in recovery problems for objects, such as functions and signals, from partial or indirect information. The recovery of objects from a set of data demands key solvers of inverse and sampling problems. Until recently, connections between the mathematical areas of inverse problems and sampling were rather tenuous. However, advances in several areas of mathematical research have revealed deep common threads between them, which proves that there is a serious need for a unifying description of the underlying mathematical ideas and concepts. Freeden and Nashed present an integrated approach to resolution methodologies from the perspective of both these areas.Researchers in sampling theory will benefit from learning about inverse problems and regularization methods, while specialists in inverse problems will gain a better understanding of the point of view of sampling concepts. This book requires some basic knowledge of functional analysis, Fourier theory, geometric number theory, constructive approximation, and special function theory. By avoiding extreme technicalities and elaborate proof techniques, it is an accessible resource for students and researchers not only from applied mathematics, but also from all branches of engineering and science.

  • Langue : anglais

    Edité par American Mathematical Society, 2023

    1470473453 / 9781470473457

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  • Langue : anglais

    Edité par American Mathematical Society, 2023

    1470473453 / 9781470473457

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    Paperback. Etat : Brand New. 491 pages. 10.00x7.05x1.14 inches. In Stock.

  • Langue : anglais

    Edité par MP-AMM American Mathematical, 2023

    1470473453 / 9781470473457

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    PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

  • Langue : anglais

    Edité par American Mathematical Society, 2023

    1470473453 / 9781470473457

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  • Langue : anglais

    Edité par American Mathematical Society, 2023

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  • Langue : anglais

    Edité par American Mathematical Society, 2023

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    Edité par American Mathematical Society, 2023

    1470473453 / 9781470473457

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  • Langue : anglais

    Edité par American Mathematical Society, 2023

    1470473453 / 9781470473457

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  • Langue : anglais

    Edité par American Mathematical Society, 2023

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  • Langue : anglais

    Edité par American Mathematical Society, 2023

    1470473453 / 9781470473457

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  • Langue : anglais

    Edité par American Mathematical Society, US, 2023

    1470473453 / 9781470473457

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    Paperback. Etat : New. The goal of this book is to introduce the reader to methodologies in recovery problems for objects, such as functions and signals, from partial or indirect information. The recovery of objects from a set of data demands key solvers of inverse and sampling problems. Until recently, connections between the mathematical areas of inverse problems and sampling were rather tenuous. However, advances in several areas of mathematical research have revealed deep common threads between them, which proves that there is a serious need for a unifying description of the underlying mathematical ideas and concepts. Freeden and Nashed present an integrated approach to resolution methodologies from the perspective of both these areas.Researchers in sampling theory will benefit from learning about inverse problems and regularization methods, while specialists in inverse problems will gain a better understanding of the point of view of sampling concepts. This book requires some basic knowledge of functional analysis, Fourier theory, geometric number theory, constructive approximation, and special function theory. By avoiding extreme technicalities and elaborate proof techniques, it is an accessible resource for students and researchers not only from applied mathematics, but also from all branches of engineering and science.

  • Langue : anglais

    Edité par American Mathematical Society, Providence, 2023

    1470473453 / 9781470473457

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    Vendeur : AussieBookSeller, Truganina, VIC, AustralieAussieBookSeller

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    EUR 217,75

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    Paperback. Etat : new. Paperback. The goal of this book is to introduce the reader to methodologies in recovery problems for objects, such as functions and signals, from partial or indirect information. The recovery of objects from a set of data demands key solvers of inverse and sampling problems. Until recently, connections between the mathematical areas of inverse problems and sampling were rather tenuous. However, advances in several areas of mathematical research have revealed deep common threads between them, which proves that there is a serious need for a unifying description of the underlying mathematical ideas and concepts. Freeden and Nashed present an integrated approach to resolution methodologies from the perspective of both these areas.Researchers in sampling theory will benefit from learning about inverse problems and regularization methods, while specialists in inverse problems will gain a better understanding of the point of view of sampling concepts. This book requires some basic knowledge of functional analysis, Fourier theory, geometric number theory, constructive approximation, and special function theory. By avoiding extreme technicalities and elaborate proof techniques, it is an accessible resource for students and researchers not only from applied mathematics, but also from all branches of engineering and science. Introduces the reader to methodologies in recovery problems for objects, such as functions and signals, from partial or indirect information. By avoiding extreme technicalities and elaborate proof techniques, this is an accessible resource for students and researchers. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.