SylabUZ
| Course name | Mathematical Programming |
| Course ID | 11.0-WK-MATED-MP-S22 |
| Faculty | Faculty of Exact and Natural Sciences |
| Field of study | Mathematics |
| Education profile | academic |
| Level of studies | Second-cycle studies leading to MS degree |
| Beginning semester | winter term 2022/2023 |
| Semester | 4 |
| ECTS credits to win | 10 |
| Course type | optional |
| Teaching language | english |
| Author of syllabus |
|
| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Lecture | 30 | 2 | - | - | Exam |
| Laboratory | 30 | 2 | - | - | Credit with grade |
| Class | 30 | 2 | - | - | Credit with grade |
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
| Outcome description | Outcome symbols | Methods of verification | The class form |
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.
Modified by dr Ewa Sylwestrzak-Maślanka (last modification: 28-02-2024 15:46)
