Projected proximal gradient trust-region algorithm for nonsmooth optimization
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915095613800448 |
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| author | Dao, Minh N. Phan, Hung M. Roberts, Lindon |
| author_facet | Dao, Minh N. Phan, Hung M. Roberts, Lindon |
| contents | We consider trust-region methods for solving optimization problems where the objective is the sum of a smooth, nonconvex function and a nonsmooth, convex regularizer. We extend the global convergence theory of such methods to include worst-case complexity bounds in the case of unbounded model Hessian growth, and introduce a new, simple nonsmooth trust-region subproblem solver based on combining several iterations of proximal gradient descent with a single projection into the trust region, which meets the sufficient descent requirements for algorithm convergence and has promising numerical results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_04889 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Projected proximal gradient trust-region algorithm for nonsmooth optimization Dao, Minh N. Phan, Hung M. Roberts, Lindon Optimization and Control We consider trust-region methods for solving optimization problems where the objective is the sum of a smooth, nonconvex function and a nonsmooth, convex regularizer. We extend the global convergence theory of such methods to include worst-case complexity bounds in the case of unbounded model Hessian growth, and introduce a new, simple nonsmooth trust-region subproblem solver based on combining several iterations of proximal gradient descent with a single projection into the trust region, which meets the sufficient descent requirements for algorithm convergence and has promising numerical results. |
| title | Projected proximal gradient trust-region algorithm for nonsmooth optimization |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2501.04889 |