A survey of conjugate gradient algorithms for solution of extreme eigen-problems of a symmetric matrix
IEEE Transactions on Acoustics Speech and Signal ProcessingPublished 1 October 1989
Xin‐She Yang, Tapan K. Sarkar, Ercüment Arvas
Citations100
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TL;DR
A survey of various conjugate gradient algorithms for the minimum/maximum eigen-problems of a fixed symmetric matrix concludes that the CG algorithms are more flexible and efficient than some of the conventional methods used in adaptive spectrum analysis and signal processing.
Abstract
A survey of various conjugate gradient (CG) algorithms is presented for the minimum/maximum eigen-problems of a fixed symmetric matrix. The CG algorithms are compared to a commonly used conventional method found in IMSL. It is concluded that the CG algorithms are more flexible and efficient than some of the conventional methods used in adaptive spectrum analysis and signal processing.>
Keywords
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