|
Teaching GuideTerm Faculty of Computer Science |
| Grao en Ciencia e Enxeñaría de Datos |
 Subjects |
| Mathematical Optimisation |
| Contents |
| Identifying Data | 2020/21 | |||||||||||||
| Subject | Mathematical Optimisation | Code | 614G02020 | |||||||||||
| Study programme |
|
|||||||||||||
| Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
| Graduate | 2nd four-month period |
Second | Obligatory | 6 | ||||||||||
|
||||||||||||||
| Topic | Sub-topic |
| Introduction to mathematical optimization. | What is an optimization problem? Types of optimization problems. |
| Linear programming. | Formulation of linear programming problems. Graphic solution of linear programming problems. The Simplex method. Duality and sensitivity analysis. Special problems of linear programming. |
| Integer linear programming. | Formulation of linear integer programming problems. Resolution methods. The branching and dimensioning algorithm Computational aspects and introduction to heuristics Special integer linear programming problems. |
| Optimization in networks. | Flow problems in networks and applications. Other network optimization problems Resolution methods. |
| Introduction to other mathematical optimization problems. | Introduction to multiobjective programming. Introduction to non-linear programming. Introduction to stochastic programming. Introduction to dynamic programming. |
|
