Advances in Polynomial Optimization
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http://hdl.handle.net/10347/29918
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Título: | Advances in Polynomial Optimization |
Autor/a: | González Rodríguez, Brais |
Dirección/Titoría: | González Díaz, Julio Pateiro López, Beatriz |
Centro/Departamento: | Universidade de Santiago de Compostela. Escola de Doutoramento Internacional (EDIUS) Universidade de Santiago de Compostela. Programa de Doutoramento en Estatística e Investigación Operativa |
Palabras chave: | Polynomial optimization | Reformulation-Linearization Technique | Algorithm | Branch and bound | Machine learning | Statistical learning | Conic optimization | Portfolio design | |
Data: | 2022 |
Resumo: | Polynomial optimization has a wide range of practical applications in fields such as optimal control, energy and water networks, facility location, management science, and finance. It also generalizes relevant optimization problems thoroughly studied in the literature, such as mixed-binary linear optimization, quadratic optimization, and complementarity problems. As finding globally optimal solutions is an extremely challenging task, the development of efficient techniques for solving polynomial optimization problems is of particular relevance. In this thesis we provide a detailed study of different techniques to solve this kind of problems and we introduce some nobel approaches in this field, including the use of statistical learning techniques. Furthermore, we also present a practical application of polynomial optimization to finance and more specifically, portfolio design. |
URI: | http://hdl.handle.net/10347/29918 |
Dereitos: | Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
Coleccións
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- Área de Ciencias [953]
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