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On solving sparse symmetric linear systems whose definiteness is unknown
| Content Provider | Semantic Scholar |
|---|---|
| Author | Marcia, Roummel F. |
| Copyright Year | 2007 |
| Abstract | Solving a large, sparse, symmetric linear system Ax = b iteratively must use appropriate methods. The conjugate gradient (CG) method can break down if A is indefinite while algorithms such as SYMMLQ and MINRES, though stable for indefinite systems, are computationally more expensive than CG when applied to positive definite matrices. In this paper, we present an iterative method for the case where the definiteness of A is not known a priori. We demonstrate that this method reduces to the CG method when applied to positive definite systems and is numerically stable when applied to indefinite systems. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://faculty.ucmerced.edu/rmarcia/pub/papers/Marcia_ASIFCG.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Algorithm Amoxicillin Analysis of algorithms Cholesky decomposition Computation (action) Conjugate gradient method Experiment FLOPS Immunostimulating conjugate (antigen) Instability Iteration Iterative method Linear system Monte Carlo method Numerical analysis Numerical stability Sparse matrix The Matrix |
| Content Type | Text |
| Resource Type | Article |
