To provide to the student sound optimization elements and the knowledge of specialized algorithms for solving different classes of difficult optimizazion problems deriving from real applications.
Prerequisites
Linear algebra and analysis
Teaching Methods
Frontal lectures
Type of Assessment
Oral exam
Course program
Decomposition algorithms for unconstrained and constrained optimization. Decomposition algorithms for machine learning. Steepest descent methods for multiobjective optimization. Nash equilibrium: models and algorithms. . LASSO methods for sparse optimization. Concave programming for minimizing the zero norm. Algorithms for multiobjective optimization.