Two-level trust-region method with random subspaces
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910595524067328 |
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| author | Angino, Andrea Kopaničáková, Alena Krause, Rolf |
| author_facet | Angino, Andrea Kopaničáková, Alena Krause, Rolf |
| contents | We introduce a two-level trust-region method (TLTR) for solving unconstrained nonlinear optimization problems. Our method uses a composite iteration step, which is based on two distinct search directions. The first search direction is obtained through minimization in the full/high-resolution space, ensuring global convergence to a critical point. The second search direction is obtained through minimization in the randomly generated subspace, which, in turn, allows for convergence acceleration. The efficiency of the proposed TLTR method is demonstrated through numerical experiments in the field of machine learning |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05479 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Two-level trust-region method with random subspaces Angino, Andrea Kopaničáková, Alena Krause, Rolf Numerical Analysis We introduce a two-level trust-region method (TLTR) for solving unconstrained nonlinear optimization problems. Our method uses a composite iteration step, which is based on two distinct search directions. The first search direction is obtained through minimization in the full/high-resolution space, ensuring global convergence to a critical point. The second search direction is obtained through minimization in the randomly generated subspace, which, in turn, allows for convergence acceleration. The efficiency of the proposed TLTR method is demonstrated through numerical experiments in the field of machine learning |
| title | Two-level trust-region method with random subspaces |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2409.05479 |