Mixed-integer linearity in nonlinear optimization: a trust region approach
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Accesso online: | |
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| _version_ | 1866913534897553408 |
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| author | De Marchi, Alberto |
| author_facet | De Marchi, Alberto |
| contents | Bringing together nonlinear optimization with polyhedral and integrality constraints enables versatile modeling, but poses significant computational challenges. We investigate a method to address these problems based on sequential mixed-integer linearization with trust region safeguard, computing feasible iterates via calls to a generic mixed-integer linear solver. Convergence to critical, possibly suboptimal, feasible points is established for arbitrary starting points. Finally, we present numerical applications in nonsmooth optimal control and optimal network design and operation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_17285 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Mixed-integer linearity in nonlinear optimization: a trust region approach De Marchi, Alberto Optimization and Control 65K05, 90C06, 90C11, 90C30 Bringing together nonlinear optimization with polyhedral and integrality constraints enables versatile modeling, but poses significant computational challenges. We investigate a method to address these problems based on sequential mixed-integer linearization with trust region safeguard, computing feasible iterates via calls to a generic mixed-integer linear solver. Convergence to critical, possibly suboptimal, feasible points is established for arbitrary starting points. Finally, we present numerical applications in nonsmooth optimal control and optimal network design and operation. |
| title | Mixed-integer linearity in nonlinear optimization: a trust region approach |
| topic | Optimization and Control 65K05, 90C06, 90C11, 90C30 |
| url | https://arxiv.org/abs/2310.17285 |