A survey of conjugate gradient algorithms for solution of extreme eigen-problems of a symmetric matrix
- 1 October 1989
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Acoustics, Speech, and Signal Processing
- Vol. 37 (10) , 1550-1556
- https://doi.org/10.1109/29.35393
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
This publication has 16 references indexed in Scilit:
- A finite step adaptive implementation of the Pisarenko's harmonic retrieval method in colored noisePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Variable Metric Method for MinimizationSIAM Journal on Optimization, 1991
- Application of the conjugate gradient and steepest descent for computing the eigenvalues of an operatorSignal Processing, 1989
- Adaptive spectral estimation by the conjugate gradient methodIEEE Transactions on Acoustics, Speech, and Signal Processing, 1986
- Adaptation convergence of spectral estimation based on Pisarenko harmonic retrievalIEEE Transactions on Acoustics, Speech, and Signal Processing, 1983
- Conjugate Direction Methods in OptimizationPublished by Springer Nature ,1980
- In Favor of Conjugate Directions: a Generalized Acceptable-point Algorithm for Function MinimizationJournal of the Franklin Institute, 1978
- The iterative calculation of several of the lowest or highest eigenvalues and corresponding eigenvectors of very large symmetric matricesJournal of Computational Physics, 1973
- Function minimization by conjugate gradientsThe Computer Journal, 1964
- A Rapidly Convergent Descent Method for MinimizationThe Computer Journal, 1963