A class of low-rank short recurrences for nonsymmetric linear matrix equations
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866915974099238912 |
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| author | Palitta, Davide Powell, Catherine E. Simoncini, Valeria |
| author_facet | Palitta, Davide Powell, Catherine E. Simoncini, Valeria |
| contents | We propose a new class of short matrix recurrences for the solution of nonsymmetric linear equations of the type $\mathbf{A}_1\mathbf{X}\mathbf{B}_1+\ldots+\mathbf{A}_p\mathbf{X}\mathbf{B}_p=CD^T$. These iterative methods combine local subspace projection to speed up convergence with rank truncation strategies and randomization procedures to limit memory consumption. Computational experiments on a benchmark problem as well as a challenging discretized mixed formulation of a diffusion equation with random inputs illustrate the potential of the proposed methodology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_01276 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A class of low-rank short recurrences for nonsymmetric linear matrix equations Palitta, Davide Powell, Catherine E. Simoncini, Valeria Numerical Analysis 65F45, 65F25, 65F99 We propose a new class of short matrix recurrences for the solution of nonsymmetric linear equations of the type $\mathbf{A}_1\mathbf{X}\mathbf{B}_1+\ldots+\mathbf{A}_p\mathbf{X}\mathbf{B}_p=CD^T$. These iterative methods combine local subspace projection to speed up convergence with rank truncation strategies and randomization procedures to limit memory consumption. Computational experiments on a benchmark problem as well as a challenging discretized mixed formulation of a diffusion equation with random inputs illustrate the potential of the proposed methodology. |
| title | A class of low-rank short recurrences for nonsymmetric linear matrix equations |
| topic | Numerical Analysis 65F45, 65F25, 65F99 |
| url | https://arxiv.org/abs/2605.01276 |