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Conjugate Gradient Algorithms in Nonconvex Optimization (Paperback, Softcover reprint of hardcover 1st ed. 2009)
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Conjugate Gradient Algorithms in Nonconvex Optimization (Paperback, Softcover reprint of hardcover 1st ed. 2009)
Series: Nonconvex Optimization and Its Applications, 89
Expected to ship within 10 - 15 working days
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Conjugate direction methods were proposed in the early 1950s. When
high speed digital computing machines were developed, attempts were
made to lay the fo- dations for the mathematical aspects of
computations which could take advantage of the ef?ciency of digital
computers. The National Bureau of Standards sponsored the Institute
for Numerical Analysis, which was established at the University of
California in Los Angeles. A seminar held there on numerical
methods for linear equationswasattendedbyMagnusHestenes,
EduardStiefel andCorneliusLanczos. This led to the ?rst
communication between Lanczos and Hestenes (researchers of the NBS)
and Stiefel (of the ETH in Zurich) on the conjugate direction
algorithm. The method is attributed to Hestenes and Stiefel who
published their joint paper in 1952 [101] in which they presented
both the method of conjugate gradient and the conjugate direction
methods including conjugate Gram-Schmidt processes. A
closelyrelatedalgorithmwasproposedbyLanczos[114]whoworkedonalgorithms
for determiningeigenvalues of a matrix. His iterative algorithm
yields the similarity transformation of a matrix into the
tridiagonal form from which eigenvalues can be well
approximated.Thethree-termrecurrencerelationofthe
Lanczosprocedurecan be obtained by eliminating a vector from the
conjugate direction algorithm scheme. Initially the conjugate
gradient algorithm was called the Hestenes-Stiefel-Lanczos method
[86].
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