The course will introduce the basic mathematical concepts necessary to
construct algorithms for the solution of optimization problems. The main
focus will be on "easy" (constrained or unconstrained, convex,
continuous) optimization problems, and their (local) solution
algorithms, relevant for applications in Data Science / Machine Learning
/ Artificial Intelligence, among others, with a view on providing to
the students solid methodological foundations useful for the several
subsequent courses in these areas. However, the course will also cover
some material related to "hard" (mixed-integer, nonconvex) optimization
problems, and their (global) solution algorithms, with a view of
providing the students with the concept of Prescriptive Analytics as
"the final step" of Data Science, and therefore a motivation for the
more advanced courses related to optimization.
- Teacher: ANTONIO FRANGIONI