Contributions to the design of polynomial optimization algorithms
In the effort of developing algorithms for solving nonlinear optimization problems, most of the research is aimed at solving general nonlinear problems. However, there is a subclass of problems, polynomial optimization problems, that it’s relevant as it provides an additional structure to the proble...
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| Tipo de recurso: | tesis doctoral |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad de Santiago de Compostela (USC) |
| Repositorio: | Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela |
| Idioma: | inglés |
| OAI Identifier: | oai:minerva.usc.gal:10347/39556 |
| Acceso en línea: | https://hdl.handle.net/10347/39556 |
| Access Level: | acceso abierto |
| Palabra clave: | Polynomial Optimization Spatial Branching Machine Learning Reformulation-Linearization Technique Optimal Power Flow 120711 Programación no lineal |
| Sumario: | In the effort of developing algorithms for solving nonlinear optimization problems, most of the research is aimed at solving general nonlinear problems. However, there is a subclass of problems, polynomial optimization problems, that it’s relevant as it provides an additional structure to the problems while still covering classes of problems often studied, such as continuous convex and nonconvex problems with quadratic costs and constraints or binary linear problems. In this thesis, we study this field of polynomial optimization and focus on three relevant aspects: solving the problem, using learning techniques to improve the performance of a solver and applying polynomial optimization techniques to a real-world problem in power network optimization. |
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