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Computing in Science & Engineering

Volume 2 Issue 1 • Jan.-Feb. 2000

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Displaying Results 1 - 13 of 13
  • Strategies for adopting FVTD on multicomputers [finite-volume time-domain analysis]

    Publication Year: 2000, Page(s):10 - 21
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (797 KB)

    Using the Message Passing Interface (MPI), Power Fortran and autotasking programming models, the authors have ported a sequential finite-volume numerical procedure, called Max3D, for solving the time-dependent (FVTD) Maxwell equations, to multicomputers. They have also demonstrated and verified a previously unobtainable high-frequency bistatic radar cross-section for a perfectly electrical conduct... View full abstract»

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  • Guest Editors Introduction to the top 10 algorithms

    Publication Year: 2000, Page(s):22 - 23
    Cited by:  Papers (14)
    Request permission for commercial reuse | PDF file iconPDF (212 KB)
    Freely Available from IEEE
  • Integer relation detection

    Publication Year: 2000, Page(s):24 - 28
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (344 KB)

    Practical algorithms for integer relation detection have become a staple in the emerging discipline of experimental mathematics-using modern computer technology to explore mathematical questions. After briefly discussing the problem of integer relation detection, the author describes several recent, remarkable applications of these techniques in both mathematics and physics. View full abstract»

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  • The (Dantzig) simplex method for linear programming

    Publication Year: 2000, Page(s):29 - 31
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (196 KB)

    In 1947, George Dantzig created a simplex algorithm to solve linear programs for planning and decision-making in large-scale enterprises. The algorithm's success led to a vast array of specializations and generalizations that have dominated practical operations research for half a century. View full abstract»

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  • Krylov subspace iteration

    Publication Year: 2000, Page(s):32 - 37
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (465 KB)

    This survey article reviews the history and current importance of Krylov subspace iteration algorithms. View full abstract»

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  • The QR algorithm

    Publication Year: 2000, Page(s):38 - 42
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (355 KB)

    After a brief sketch of the early days of eigenvalue hunting, the author describes the QR (or orthogonal triangular) matrix factorization algorithm and its major virtues. The symmetric case brings with it guaranteed convergence and an elegant implementation. An account of the impressive discovery of the algorithm brings the article to a close. View full abstract»

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  • A perspective on Quicksort

    Publication Year: 2000, Page(s):43 - 49
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (490 KB)

    This article introduces the basic Quicksort algorithm and gives a flavor of the richness of its complexity analysis. The author also provides a glimpse of some of its generalizations to parallel algorithms and computational geometry. View full abstract»

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  • The decompositional approach to matrix computation

    Publication Year: 2000, Page(s):50 - 59
    Cited by:  Papers (13)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (671 KB)

    The introduction of matrix decomposition into numerical linear algebra revolutionized matrix computations. The article outlines the decompositional approach, comments on its history, and surveys the six most widely used decompositions: Cholesky decomposition; pivoted LU decomposition; QR decomposition; spectral decomposition; Schur decomposition; and singular value decomposition. View full abstract»

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  • The FFT: an algorithm the whole family can use

    Publication Year: 2000, Page(s):60 - 64
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (384 KB)

    The fast Fourier transform is one of the fundamental algorithm families in digital information processing. The author discusses its past, present, and future, along with its important role in our current digital revolution. It is concluded that the FFT is both parent and child of the digital revolution, a computational technique at the nexus of the worlds of business and entertainment, national se... View full abstract»

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  • The Metropolis Algorithm

    Publication Year: 2000, Page(s):65 - 69
    Cited by:  Papers (22)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (391 KB)

    The Metropolis Algorithm has been the most successful and influential of all the members of the computational species that used to be called the "Monte Carlo method". Today, topics related to this algorithm constitute an entire field of computational science supported by a deep theory and having applications ranging from physical simulations to the foundations of computational complexity. Since th... View full abstract»

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  • The Fortran I compiler

    Publication Year: 2000, Page(s):70 - 75
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (456 KB)

    The Fortran I compiler was the first demonstration that it is possible to automatically generate efficient machine code from high-level languages. It has thus been enormously influential. The article presents a brief description of the techniques used in the Fortran I compiler for the parsing of expressions, loop optimization, and register allocation. View full abstract»

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  • The fast multipole algorithm

    Publication Year: 2000, Page(s):76 - 79
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (339 KB)

    Accurate computation of the mutual interactions of N particles through electrostatic or gravitational forces has impeded progress in many areas of simulation science. The fast multipole algorithm (FMA) provides an efficient scheme for reducing computational complexity. Researchers are studying very large astrophysical simulations with hybrids of the FMA and the earlier Barnes-Hut scheme (J.E. Barn... View full abstract»

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  • Gauss-Legendre principal value integration

    Publication Year: 2000, Page(s):92 - 95
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1148 KB)

    As we attempt more sophisticated projects in science and engineering, the mathematical tools we apply to them also become more sophisticated. Because so few problems lend themselves to closed-form solution, we often need to convert formal definitions into practical numerical methods. One such problem deals with the principal value integral, which many students encounter in courses on functions of ... View full abstract»

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Aims & Scope

The computational and data-centric problems faced by scientists and engineers transcend disciplines. There is a need to share knowledge of algorithms, software, and architectures, and to transmit lessons-learned to a broad scientific audience. Computing in Science & Engineering (CiSE) is a cross-disciplinary, international publication that meets this need by presenting contributions of high interest and educational value from a variety of fields, including—but not limited to—physics, biology, chemistry, and astronomy. CiSE emphasizes innovative applications in advanced computing, simulation, and analytics, among other cutting-edge techniques. CiSE publishes peer-reviewed research articles, and also runs departments spanning news and analyses, topical reviews, tutorials, case studies, and more.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Jim X. Chen
George Mason University
jchen@cs.gmu.edu