Hybrid methods for large sparse nonlinear least squares
✍ Scribed by L. Lukšan
- Publisher
- Springer
- Year
- 1996
- Tongue
- English
- Weight
- 916 KB
- Volume
- 89
- Category
- Article
- ISSN
- 0022-3239
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We propose to precondition the GMRES method by using the incomplete Givens orthogonalization (IGO) method for the solution of large sparse linear least-squares problems. Theoretical analysis shows that the preconditioner satisfies the sufficient condition that can guarantee that the preconditioned G
For linear least squares problems min x Ax -b 2 , where A is sparse except for a few dense rows, a straightforward application of Cholesky or QR factorization will lead to catastrophic fill in the factor R. We consider handling such problems by a matrix stretching technique, where the dense rows ar