Methods for big data and machine learning

Course objectives

General targets: to acquire basic and advanced knowledge and hands-on experience on some classic topics in finite-dimensional optimization. Specific targets Knowledge and understanding: Understanding of the theoretical foundations of optimization theory and of the main algorithm classes for the solution of optimization problems. Applying knowledge and understanding: the student will be able to identify relevant characteristics of optimization problems and to select the most appropriate solution method for a given problem, also taking into account practical constraints due to the applicative environment (for example, the required accuracy or time limits). In addition the student will be able to correctly analyze the results provided by commercial or ad-hoc resolution software. Making judgements: ability to enucleate the most significant aspects of an optimization problem and of its solution methods. Communication skills: ability to enucleate the significant points of the theory, to know how to illustrate the most interesting parts with appropriate examples, to discuss mathematically the most subtle points. Learning skills: the acquired knowledge will allow the student to undertake more advanced studies in optimization and to be able to work in industrial and research environments where optimization is used.

Channel 1
MARCO SCIANDRONE Lecturers' profile

Program - Frequency - Exams

Course program
Mathematical programming models Optimality conditions Convex programming Linear programming Nonlinear programming Algorithms for unconstrained optimization Algorithms for constrained optimization Methods for large-scale problems
Prerequisites
Linear Algebra Function of several variables
Books
Introduction to Methods for Nonlinear Optimization, L. Grippo and M. Sciandrone, Springer, 2023
Teaching mode
Frontal teaching
Frequency
In presence
Exam mode
Oral exam with theory questions and exercises
Bibliography
Introduction to Linear Optimization, D. Bertsimas and J. Tsitsiklis, 2008. Nonlinear programming, D. Bertsekas, third edition, 2016.
Lesson mode
Frontal teaching
  • Academic year2024/2025
  • CourseApplied Mathematics
  • CurriculumMatematica per Data Science
  • Year1st year
  • Semester2nd semester
  • SSDMAT/09
  • CFU6
  • Subject areaAttività formative affini o integrative