Course Details for A.Y. 2019/2020

Course Objectives

Understand the role of combinatorial optimization models in technical-scientific applications. Formulate and solve combinatorial optimization problems in terms of 01 linear optimization. Get familiar with some fundamental algorithms for specific problems.

Course Content

• Decision processes: definitions and phases
• Deciding and valuing: relations and numbers, preferences as relations, utilities as values, Pareto curves.
• Representation of individual decisions: alternatives, decision matrices, zero-sum games, Stackelberg games.
• (Integer) Linear Programming models and algorithms.
• Decision making under uncertainty/ignorance: stochastic, Bayesian and robust models.
• Social decision theory.
• Decision modello and data analysis: single-multi decision maker, single-multi objective. Descriptive, predicative and prescriptive models, examples. Linear and non-lineare regression as a linear optimization problem, the minimum square method. Classification problems (p-median, p-centre). Separation and Overlap.

Prerequisites and Learning Activities

Basics of Linear Algebra. Linear operators, finite-dimensional matrices and vectors.

Assessment Methods and Criteria

Written test (possibly divided into two intermediate tests) + oral test

Notes

• Office hours: Thursday from 14:00 to 15:30 pm

Course page updates

• A.Y. 2017/2018
• A.Y. 2018/2019
• A.Y. 2019/2020