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Teaching GuideTerm Faculty of Computer Science |
| Grao en Ciencia e Enxeñaría de Datos |
 Subjects |
| Mathematical Optimisation |
| Methodologies |
| Identifying Data | 2022/23 | |||||||||||||
| Subject | Mathematical Optimisation | Code | 614G02020 | |||||||||||
| Study programme |
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| Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
| Graduate | 2nd four-month period |
Second | Obligatory | 6 | ||||||||||
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| Methodologies | Description |
| Guest lecture / keynote speech | The student will receive master classes in which the teacher, with the help of the relevant audiovisual media, will explain the theoretical and practical contents of the subject. Participation and debate will be encouraged at all times. |
| Laboratory practice | In the laboratory practices, students will learn to use the basic optimization tools: linear programming solvers, general linear programming interfaces and algebraic modeling languages. These tools are valid for several programming languages, but in this subject R, Julia and Python will be fundamentally taken into account. |
| Seminar | The seminars will reinforce both the applied nature of the subject and its interactivity. In the seminars the students will be able to expose their doubts and worries referred to the subject, and will have the opportunity to carry out, with the supervision of the teacher, problems similar to those of the exams. |
| Mixed objective/subjective test | The students must demonstrate their mastery of the theoretical aspects of the subject and their ability to solve problems in the field of optimization. |
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