An inexact semismooth Newton method with application to adaptive randomized sketching for dynamic optimization
In many applications, one can only access the inexact gradients and inexact hessian times vector products. Thus it is essential to consider algorithms that can handle such inexact quantities with a guaranteed convergence to solution. An inexact adaptive and provably convergent semismooth Newton meth...
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| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
1 January 2024
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| In: |
Finite elements in analysis and design
Year: 2024, Jahrgang: 228, Pages: 1-21 |
| ISSN: | 0168-874X |
| DOI: | 10.1016/j.finel.2023.104052 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.finel.2023.104052 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S0168874X23001452 
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| Verfasserangaben: | Mohammed Alshehri, Harbir Antil, Evelyn Herberg, Drew P. Kouri |
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| 245 | 1 | 3 | |a An inexact semismooth Newton method with application to adaptive randomized sketching for dynamic optimization |c Mohammed Alshehri, Harbir Antil, Evelyn Herberg, Drew P. Kouri |
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| 520 | |a In many applications, one can only access the inexact gradients and inexact hessian times vector products. Thus it is essential to consider algorithms that can handle such inexact quantities with a guaranteed convergence to solution. An inexact adaptive and provably convergent semismooth Newton method is considered to solve constrained optimization problems. In particular, dynamic optimization problems, which are known to be highly expensive, are the focus. A memory efficient semismooth Newton algorithm is introduced for these problems. The source of efficiency and inexactness is the randomized matrix sketching. Applications to optimization problems constrained by partial differential equations are also considered. | ||
| 650 | 4 | |a Adaptivity | |
| 650 | 4 | |a Compression methods | |
| 650 | 4 | |a Convergence analysis | |
| 650 | 4 | |a Inexact gradient and Hessian | |
| 650 | 4 | |a Measure control | |
| 650 | 4 | |a Nonsmooth optimization | |
| 650 | 4 | |a Randomized sketching | |
| 650 | 4 | |a Semismooth Newton | |
| 650 | 4 | |a Variational discretization | |
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| 700 | 1 | |a Herberg, Evelyn Christin |d 1993- |e VerfasserIn |0 (DE-588)1238800696 |0 (DE-627)1766518796 |4 aut | |
| 700 | 1 | |a Kouri, Drew P. |e VerfasserIn |4 aut | |
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