ISBN: 9781611974379 - minimum-volume ellipsoids: theory and algorithms (20 risultati)

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

Condizione: New.

Paperback. Condizione: New. This book, the first on these topics, addresses the problem of finding an ellipsoid to represent a large set of points in high-dimensional space, which has applications in computational geometry, data representations, and optimal design in statistics. The book covers the formulation of this and related problems, theoretical properties of their optimal solutions, and algorithms for their solution. Due to the high dimensionality of these problems, first-order methods that require minimal computational work at each iteration are attractive. While algorithms of this kind have been discovered and rediscovered over the past fifty years, their computational complexities and convergence rates have only recently been investigated. The optimization problems in the book have the entries of a symmetric matrix as their variables, so the author's treatment also gives an introduction to recent work in matrix optimization.Provides historical perspective on the problems studied by optimizers, statisticians, and geometric functional analysts.Demonstrates the huge computational savings possible by exploiting simple updates for the determinant and the inverse after a rank-one update.Highlights the difficulties in algorithms when related problems are studied that do not allow simple updates at each iteration.Gives rigorous analyses of the proposed algorithms, MATLAB codes, and computational results.…

Condizione: new.

Condizione: As New. Unread book in perfect condition.

Condizione: New. 2016. Paperback. . . . . .

Condizione: New.

Paperback. Condizione: Brand New. 1st edition. 163 pages. 10.00x6.90x0.40 inches. In Stock.

Paperback. Condizione: new. Paperback. This book, the first on these topics, addresses the problem of finding an ellipsoid to represent a large set of points in high-dimensional space, which has applications in computational geometry, data representations, and optimal design in statistics. The book covers the formulation of this and related problems, theoretical properties of their optimal solutions, and algorithms for their solution. Due to the high dimensionality of these problems, first-order methods that require minimal computational work at each iteration are attractive. While algorithms of this kind have been discovered and rediscovered over the past fifty years, their computational complexities and convergence rates have only recently been investigated. The optimization problems in the book have the entries of a symmetric matrix as their variables, so the authors treatment also gives an introduction to recent work in matrix optimization.Provides historical perspective on the problems studied by optimizers, statisticians, and geometric functional analysts.Demonstrates the huge computational savings possible by exploiting simple updates for the determinant and the inverse after a rank-one update.Highlights the difficulties in algorithms when related problems are studied that do not allow simple updates at each iteration.Gives rigorous analyses of the proposed algorithms, MATLAB codes, and computational results. Addresses the problem of finding an ellipsoid to represent a large set of points in high-dimensional space, which has applications in computational geometry, data representations, and optimal design in statistics. The book covers the formulation of this and related problems, theoretical properties of their optimal solutions, and algorithms for their solution. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condizione: New. pp. 163.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: New. New copy - Usually dispatched within 4 working days.

Condizione: New. pp. 163.

Condizione: New. 2016. Paperback. . . . . . Books ship from the US and Ireland.

Condizione: New. In English.

Kartoniert / Broschiert. Condizione: New.

Taschenbuch. Condizione: Neu. Neuware - The first book in the area, this volume addresses the problem of finding an ellipsoid to represent a large set of points in high-dimensional space.

Paperback. Condizione: New. This book, the first on these topics, addresses the problem of finding an ellipsoid to represent a large set of points in high-dimensional space, which has applications in computational geometry, data representations, and optimal design in statistics. The book covers the formulation of this and related problems, theoretical properties of their optimal solutions, and algorithms for their solution. Due to the high dimensionality of these problems, first-order methods that require minimal computational work at each iteration are attractive. While algorithms of this kind have been discovered and rediscovered over the past fifty years, their computational complexities and convergence rates have only recently been investigated. The optimization problems in the book have the entries of a symmetric matrix as their variables, so the author's treatment also gives an introduction to recent work in matrix optimization.Provides historical perspective on the problems studied by optimizers, statisticians, and geometric functional analysts.Demonstrates the huge computational savings possible by exploiting simple updates for the determinant and the inverse after a rank-one update.Highlights the difficulties in algorithms when related problems are studied that do not allow simple updates at each iteration.Gives rigorous analyses of the proposed algorithms, MATLAB codes, and computational results.…

Paperback. Condizione: new. Paperback. This book, the first on these topics, addresses the problem of finding an ellipsoid to represent a large set of points in high-dimensional space, which has applications in computational geometry, data representations, and optimal design in statistics. The book covers the formulation of this and related problems, theoretical properties of their optimal solutions, and algorithms for their solution. Due to the high dimensionality of these problems, first-order methods that require minimal computational work at each iteration are attractive. While algorithms of this kind have been discovered and rediscovered over the past fifty years, their computational complexities and convergence rates have only recently been investigated. The optimization problems in the book have the entries of a symmetric matrix as their variables, so the authors treatment also gives an introduction to recent work in matrix optimization.Provides historical perspective on the problems studied by optimizers, statisticians, and geometric functional analysts.Demonstrates the huge computational savings possible by exploiting simple updates for the determinant and the inverse after a rank-one update.Highlights the difficulties in algorithms when related problems are studied that do not allow simple updates at each iteration.Gives rigorous analyses of the proposed algorithms, MATLAB codes, and computational results. Addresses the problem of finding an ellipsoid to represent a large set of points in high-dimensional space, which has applications in computational geometry, data representations, and optimal design in statistics. The book covers the formulation of this and related problems, theoretical properties of their optimal solutions, and algorithms for their solution. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

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