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Conjugate Direction Methods in Optimization (Stochastic Modelling and Applied Probability, 12)

Conjugate Direction Methods in Optimization (Stochastic Modelling and Applied Probability, 12)

Conjugate Direction Methods in Optimization (Stochastic Modelling and Applied
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Conjugate Direction Methods in Optimization (Stochastic Modelling and Applied Probability, 12) Hardback - 1980 - 1980th Edition

by Hestenes, M.R

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From the publisher

Shortly after the end of World War II high-speed digital computing machines were being developed. It was clear that the mathematical aspects of com- putation needed to be reexamined in order to make efficient use of high-speed digital computers for mathematical computations. Accordingly, under the leadership of Min a Rees, John Curtiss, and others, an Institute for Numerical Analysis was set up at the University of California at Los Angeles under the sponsorship of the National Bureau of Standards. A similar institute was formed at the National Bureau of Standards in Washington, D. C. In 1949 J. Barkeley Rosser became Director of the group at UCLA for a period of two years. During this period we organized a seminar on the study of solu- tions of simultaneous linear equations and on the determination of eigen- values. G. Forsythe, W. Karush, C. Lanczos, T. Motzkin, L. J. Paige, and others attended this seminar. We discovered, for example, that even Gaus- sian elimination was not well understood from a machine point of view and that no effective machine oriented elimination algorithm had been developed. During this period Lanczos developed his three-term relationship and I had the good fortune of suggesting the method of conjugate gradients. We dis- covered afterward that the basic ideas underlying the two procedures are essentially the same. The concept of conjugacy was not new to me. In a joint paper with G. D.

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