Solving sparse symmetric generalized eigenvalue problems without factorization
Technical Report
·
OSTI ID:5879482
An iterative technique is discussed for finding the algebraically smallest (or largest) eigenvalue of the generalized eigenvalue problem A - lambdaM, where A and M are real and symmetric, and M is positive definite. It is assumed that A and M are such that it is undesirable to factor the matrix A - sigmaM for any value of sigma. It is proved that the algorithm is globally convergent and that convergence is asymptotically quadratic. Finally, the modifications required in the algorithm to make it computationally feasible are discussed. 7 tables.
- Research Organization:
- Oak Ridge National Lab., TN (USA)
- DOE Contract Number:
- W-7405-ENG-26
- OSTI ID:
- 5879482
- Report Number(s):
- ORNL/CSD-49
- Country of Publication:
- United States
- Language:
- English
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