SylabUZ
Nazwa przedmiotu | Mathematical Programming |
Kod przedmiotu | 11.0-WK-MATED-MP-S22 |
Wydział | Wydział Matematyki, Informatyki i Ekonometrii |
Kierunek | Mathematics |
Profil | ogólnoakademicki |
Rodzaj studiów | drugiego stopnia z tyt. magistra |
Semestr rozpoczęcia | semestr zimowy 2022/2023 |
Semestr | 4 |
Liczba punktów ECTS do zdobycia | 10 |
Typ przedmiotu | obieralny |
Język nauczania | angielski |
Sylabus opracował |
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Forma zajęć | Liczba godzin w semestrze (stacjonarne) | Liczba godzin w tygodniu (stacjonarne) | Liczba godzin w semestrze (niestacjonarne) | Liczba godzin w tygodniu (niestacjonarne) | Forma zaliczenia |
Wykład | 30 | 2 | - | - | Egzamin |
Laboratorium | 30 | 2 | - | - | Zaliczenie na ocenę |
Ćwiczenia | 30 | 2 | - | - | Zaliczenie na ocenę |
Students learn methods for solving constrained optimization problems, in particular linear programming and quadratic programming problems. They will learn the basics of multi-criteria optimization and non-differentiable minimization. In addition, they will become familiar with the appropriate software.
Linear Algebra 1 and 2, Calculus 1 and 2, Fundamentals of Optimization.
Linear programming. A linear programming (ZPL) task and tasks that can be reduced to ZPL. Graphical method. Simplex algorithm, phase I and II. Duality and dual simplex algorithm.
Quadratic programming. Methods used for equality and inequality constraints, active constraints method.
Constrained minimization methods. Reduction to minimization without constraints: penalty function and barrier function. SQP method.
Multi-criteria linear programming. Multi-criteria linear programming task. Pareto-optimal solutions. Optimal solutions due to the meta-criterion.
Non-differentiable convex minimization. Problems in non-differentiable minimization. Monotonicity in Fejer's sense. Optimality conditions. Subgradient projection method.
Traditional lecture; auditorium exercises in which students solve tasks; a laboratory in which students become familiar with software used to solve mathematical programming tasks
Opis efektu | Symbole efektów | Metody weryfikacji | Forma zajęć |
Exercises: checking the level of students' preparation and their activity during classes; colloquium with tasks of varying difficulty, allowing you to assess whether the student has achieved the learning outcomes.
Laboratory: checking the level of students' preparation and their activity during classes; colloquium with tasks of varying difficulty; checking whether the student knows how to use the appropriate software.
Lecture: written exam consisting of test questions and tasks, verifying understanding of models and methods.
The final grade for the course takes into account the grade for exercises (30%), laboratory (30%) and exam grade (40%).
The condition for passing the course is positive grades from exercises, laboratory and exam.
Zmodyfikowane przez dr Ewa Sylwestrzak-Maślanka (ostatnia modyfikacja: 28-02-2024 15:46)