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Synopsis
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, 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.
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About the Author
Michael J. Todd is Leon C. Welch Professor Emeritus of the School of Operations Research and Information Engineering, Cornell University. He received a Guggenheim Fellowship, 1980-1981; a Sloan Research Fellowship, 1981-1985; the George B. Dantzig Prize, 1988; and the John von Neumann Theory Prize, 2003. He is an INFORMS Fellow and a SIAM Fellow. He has served on the editorial boards of Mathematics of Operations Research, Operations Research, and SIAM Journal on Optimization. He was Managing Editor of Foundations of Computational Mathematics and served on the boards of Acta Numerica and Foundations and Trends in Optimization. He is the author of one and co-editor of five previous books.
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Minimum-volume Ellipsoids : Theory and Algorithms
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Paperback. Condition: 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. Seller Inventory # LU-9781611974379
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Paperback. Condition: 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. Seller Inventory # 9781611974379
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MPS-SIAM Series on Optimization: Series Number 23: Minimum-Volume Ellipsoids: Theory and Algorithms
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MinimumVolume Ellipsoids Theory and Algorithms
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Minimum-Volume Ellipsoids: Theory and Algorithms (MPS-SIAM Series on Optimization)
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