RT Dissertation/Thesis
T1 Contributions to the design of polynomial optimization algorithms
A1 Gómez Casares, Ignacio
K1 Polynomial Optimization
K1 Spatial Branching
K1 Machine Learning
K1 Reformulation-Linearization Technique
K1 Optimal Power Flow
AB 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.
YR 2024
FD 2024
LK https://hdl.handle.net/10347/39556
UL https://hdl.handle.net/10347/39556
LA eng
DS Minerva
RD 3 may 2026