Statistical Sciences

Learning goals The course aims to provide a unified view of the main optimization problems and related solution algorithms. At the end of the course the student is able to classify optimization problems in appropriate categories, formulate optimization models for simple real problems and solve them with the appropriate algorithms and software. Knowledge and understanding. After attending the course the student knows and understands the different classes of optimization problems (Linear Programming, Integer Linear, Non-Linear Convex Programming) and the main solution methods (Simplex Method, Branch and Bound, Cutting Plane, Gradient-based descent methods and Interior point methods). Applying knowledge and understanding. At the end of the course the students are able to recognize real problems that can be modeled as optimization problems and to solve them with the appropriate algorithms and software. Making judgements. Students acquire the ability to classify optimization problems in appropriate categories and to evaluate their computational complexity. They also learn to explore the various aspects related to application problems, to evaluate different modeling options and to analyze the results obtained. Communication skills. Attending the lessons and studying the course material the students acquire the basic language of the discipline. Laboratory activities allow students to acquire the ability to prepare brief documents describing modeling choices and results of a simple case study. Learning skills. After the exam the students are able to attend courses with the various classes of optimization problems.