Abstract
From the formulas of the conjugate gradient, a similarity between a symmetric positive definite (SPD) matrix A and a tridiagonal matrix B is obtained. The elements of the matrix B are determined by the parameters of the conjugate gradient. The computation of eigenvalues of A is then reduced to the case of the tridiagonal matrix B. The approximation of extreme eigenvalues of A can be obtained as a ‘by-product’ in the computation of the conjugate gradient if a computational cost of O(s) arithmetic operations is added where s is the number of iterations. This computational cost is negligible compared with the conjugate gradient. If the matrix A is not SPD, the approximation of the condition number of A can be obtained from the computation of the conjugate gradient on AT A. Numerical results show that this is a convenient and highly efficient method for computing extreme eigenvalues and the condition number of nonsingular matrices.
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Communicated by Qin Yuan-xun
Project supported by the National Natural Science Foundation of China
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Guang-yao, L. On the approximate computation of extreme eigenvalues and the condition number of nonsingular matrices. Appl Math Mech 13, 199–204 (1992). https://doi.org/10.1007/BF02454243
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DOI: https://doi.org/10.1007/BF02454243