Numerical Linear Algebra: Least Squares, QR and SVD
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913628673802240 |
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| author | Mirzaei, Davoud |
| author_facet | Mirzaei, Davoud |
| contents | These lecture notes focus on some numerical linear algebra algorithms in scientific computing. We assume that students are familiar with elementary linear algebra concepts such as vector spaces, systems of equations, matrices, norms, eigenvalues, and eigenvectors. In the numerical part, we do not pursue Gaussian elimination and other LU factorization algorithms for square systems. Instead, we mainly focus on overdetermined systems, least squares solutions, orthogonal factorizations, and some applications to data analysis and other areas. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_19960 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Numerical Linear Algebra: Least Squares, QR and SVD Mirzaei, Davoud History and Overview Numerical Analysis These lecture notes focus on some numerical linear algebra algorithms in scientific computing. We assume that students are familiar with elementary linear algebra concepts such as vector spaces, systems of equations, matrices, norms, eigenvalues, and eigenvectors. In the numerical part, we do not pursue Gaussian elimination and other LU factorization algorithms for square systems. Instead, we mainly focus on overdetermined systems, least squares solutions, orthogonal factorizations, and some applications to data analysis and other areas. |
| title | Numerical Linear Algebra: Least Squares, QR and SVD |
| topic | History and Overview Numerical Analysis |
| url | https://arxiv.org/abs/2412.19960 |