The aim of the course is that of studying and analyzing sophisticated and efficient optimization algorithms for the solution of nonstandard problems characterized, for instance, by a huge number of variables, by the presence of high nonlinearities and of noise, by the presence of both continuous and discrete variables,
by the precesence of different decision makers
To provide to the student theoretical and practical bases for the solution of different classes of problems of applied sciences whose difficulty may be due, for instance, to the huge number of variables, to the presence of high nonlinearities and noise, to the availability of partial information on the problem.
The theoretical and practical aspects of the course will make the student capable to suitably employ sophisticated optimization algorithms for the solution of complex problems, and to design new algorithms.
Prerequisites
Linear algebra, mathematical analysis
Teaching Methods
Frontal lectures
Type of Assessment
Oral discussion
Course program
Decomposition methods for large scale problems
Online optimization methods
Global optimization methods
Games theory
Methods for equilibrium problems
Methods for mixed integer nonlinear programming
Methods for "sparse" optimization
Applications to: machine learning, control, telecommunication networks