First order methods optimization par beck amir (20 résultats)

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    Edité par SIAM-Society for Industrial and Applied Mathematicsq, 2017

    1611974984 / 9781611974980

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    Edité par SIAM-Society for Industrial and Applied Mathematicsq, 2017

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    Edité par SIAM-Society for Industrial and Applied Mathematicsq, 2017

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    Edité par SIAM - Society for Industrial and Applied Mathematics, 2017

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  • Langue : anglais

    Edité par SIAM-Society for Industrial and Applied Mathematicsq, 2017

    1611974984 / 9781611974980

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  • Langue : anglais

    Edité par Society for Industrial and Applied Mathematics,U.S., US, 2017

    1611974984 / 9781611974980

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    Paperback. Etat : New. The primary goal of this book is to provide a self-contained, comprehensive study of the main ?rst-order methods that are frequently used in solving large-scale problems. First-order methods exploit information on values and gradients/subgradients (but not Hessians) of the functions composing the model under consideration. With the increase in the number of applications that can be modeled as large or even huge-scale optimization problems, there has been a revived interest in using simple methods that require low iteration cost as well as low memory storage.The author has gathered, reorganized, and synthesized (in a unified manner) many results that are currently scattered throughout the literature, many of which cannot be typically found in optimization books.First-Order Methods in Optimization offers comprehensive study of first-order methods with the theoretical foundations; provides plentiful examples and illustrations; emphasizes rates of convergence and complexity analysis of the main first-order methods used to solve large-scale problems; and covers both variables and functional decomposition methods.…

  • Langue : anglais

    Edité par Society for Industrial & Applied Mathematics,U.S., New York, 2017

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    Paperback. Etat : new. Paperback. The primary goal of this book is to provide a self-contained, comprehensive study of the main ?rst-order methods that are frequently used in solving large-scale problems. First-order methods exploit information on values and gradients/subgradients (but not Hessians) of the functions composing the model under consideration. With the increase in the number of applications that can be modeled as large or even huge-scale optimization problems, there has been a revived interest in using simple methods that require low iteration cost as well as low memory storage.The author has gathered, reorganized, and synthesized (in a unified manner) many results that are currently scattered throughout the literature, many of which cannot be typically found in optimization books.First-Order Methods in Optimization offers comprehensive study of first-order methods with the theoretical foundations; provides plentiful examples and illustrations; emphasizes rates of convergence and complexity analysis of the main first-order methods used to solve large-scale problems; and covers both variables and functional decomposition methods. Provides a self-contained, comprehensive study of the main first-order methods that are frequently used in solving large-scale problems. The author has gathered, reorganized, and synthesized (in a unified manner) many results that are currently scattered throughout the literature, many of which cannot be typically found in optimization books. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

  • Langue : anglais

    Edité par SIAM-Society for Industrial and Applied Mathematicsq, 2017

    1611974984 / 9781611974980

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    Edité par Society for Industrial & Applied Mathematics, 2017

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    Etat : New. pp. 484.

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    Edité par SIAM - Society for Industrial and Applied Mathematics, 2017

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    Edité par Society for Industrial & Applied Mathematics,U.S., 2017

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    Edité par Society for Industrial & Applied Mathematics, 2017

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    Edité par SIAM - Society for Industrial and Applied Mathematics, 2017

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    Etat : New. Provides a self-contained, comprehensive study of the main first-order methods that are frequently used in solving large-scale problems. The author has gathered, reorganized, and synthesized (in a unified manner) many results that are currently scattered th.

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    Edité par SIAM-Society for Industrial and Applied Mathematicsq, 2017

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  • Langue : anglais

    Edité par Society For Industrial & Applied Mathematics,U.S. Nov 2017, 2017

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    Taschenbuch. Etat : Neu. Neuware - Provides a self-contained, comprehensive study of the main first-order methods that are frequently used in solving large-scale problems. The author has gathered, reorganized, and synthesized (in a unified manner) many results that are currently scattered throughout the literature, many of which cannot be typically found in optimization books.…

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    Edité par Society for Industrial and Applied Mathematics,U.S., US, 2017

    1611974984 / 9781611974980

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    Paperback. Etat : New. The primary goal of this book is to provide a self-contained, comprehensive study of the main ?rst-order methods that are frequently used in solving large-scale problems. First-order methods exploit information on values and gradients/subgradients (but not Hessians) of the functions composing the model under consideration. With the increase in the number of applications that can be modeled as large or even huge-scale optimization problems, there has been a revived interest in using simple methods that require low iteration cost as well as low memory storage.The author has gathered, reorganized, and synthesized (in a unified manner) many results that are currently scattered throughout the literature, many of which cannot be typically found in optimization books.First-Order Methods in Optimization offers comprehensive study of first-order methods with the theoretical foundations; provides plentiful examples and illustrations; emphasizes rates of convergence and complexity analysis of the main first-order methods used to solve large-scale problems; and covers both variables and functional decomposition methods.…

  • Langue : anglais

    Edité par Society for Industrial & Applied Mathematics,U.S., New York, 2017

    1611974984 / 9781611974980

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    Paperback. Etat : new. Paperback. The primary goal of this book is to provide a self-contained, comprehensive study of the main ?rst-order methods that are frequently used in solving large-scale problems. First-order methods exploit information on values and gradients/subgradients (but not Hessians) of the functions composing the model under consideration. With the increase in the number of applications that can be modeled as large or even huge-scale optimization problems, there has been a revived interest in using simple methods that require low iteration cost as well as low memory storage.The author has gathered, reorganized, and synthesized (in a unified manner) many results that are currently scattered throughout the literature, many of which cannot be typically found in optimization books.First-Order Methods in Optimization offers comprehensive study of first-order methods with the theoretical foundations; provides plentiful examples and illustrations; emphasizes rates of convergence and complexity analysis of the main first-order methods used to solve large-scale problems; and covers both variables and functional decomposition methods. Provides a self-contained, comprehensive study of the main first-order methods that are frequently used in solving large-scale problems. The author has gathered, reorganized, and synthesized (in a unified manner) many results that are currently scattered throughout the literature, many of which cannot be typically found in optimization books. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. …