RT Dissertation/Thesis
T1 Advances in Polynomial Optimization
A1 González Rodríguez, Brais
K1 Polynomial optimization
K1 Reformulation-Linearization Technique
K1 Algorithm
K1 Branch and bound
K1 Machine learning
K1 Statistical learning
K1 Conic optimization
K1 Portfolio design
AB Polynomial optimization has a wide range of practical applications in fieldssuch as optimal control, energy and water networks, facility location, management science, and finance. It alsogeneralizes relevant optimization problems thoroughly studied in the literature, such as mixed-binary linearoptimization, quadratic optimization, and complementarity problems. As finding globally optimal solutions is anextremely challenging task, the development of efficient techniques for solving polynomial optimization problems isof particular relevance. In this thesis we provide a detailed study of different techniques to solve this kind ofproblems 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.
YR 2022
FD 2022
LK http://hdl.handle.net/10347/29918
UL http://hdl.handle.net/10347/29918
LA eng
DS Minerva
RD 23 abr 2026