An algorithm for solving sparse Nonlinear Least Squares problems
✍ Scribed by J. M. Martínez
- Publisher
- Springer Vienna
- Year
- 1987
- Tongue
- English
- Weight
- 809 KB
- Volume
- 39
- Category
- Article
- ISSN
- 0010-485X
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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
A sparse QR-factorization algorithm SPARQR for coarse-grained parallel computations is described. The coefficient matrix, which is assumed to be general sparse, is reordered in an attempt to bring as many zero elements in the lower left corner as possible. The reordered matrix is then partitioned in