Course: Optimisation

» List of faculties » FAI » AUM
Course title Optimisation
Course code AUM/AP7OP
Organizational form of instruction Lecture + Lesson
Level of course Master
Year of study not specified
Semester Winter
Number of ECTS credits 5
Language of instruction Czech
Status of course Compulsory
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Prokop Roman, prof. Ing. CSc.
  • Pekař Libor, doc. Ing. Ph.D.
Course content
1. Static optimization, objectives, history, basic concepts and notions, famous personalities and achievements. 2. Unconstrained problem, single and multivariable case. Derivatives, gradient, Hessian and Jacobian. 3. Constrained problem with a set of equalities. Lagrange method. 4. Nonlinear inequality constrained problem. Kuhn Tucker theorem. 5. Iterative methods withou derivatives, searching procedures. Fibbonaci method, Box - Wilson and Nelder - Mead methods. 6. Gradient methods, Gauss - Seidel method, Newton´s method. 7. Least - squares optimization. Gauss - Newton´s method, Levenberg - Marquart method. 8. Linear programming. Formulation and classification. 9. Basic simplex algorithm. Examples. 10. Advanced linear programming, mixed constraints. 11. Integer linear programming. Formulation and solution. 12. Dynamic programming. Bellman principle. . 13. Game theory. Basic concepts, classification. Reduction to linear programming. 14. Multiobjective, convex and decision problems.

Learning activities and teaching methods
Methods for written tasks (e.g. comprehensive exams, written tests), Demonstration, Exercises on PC, Individual work of students
learning outcomes
Knowledge
explain unconstrained and constrained optimization problems
explain unconstrained and constrained optimization problems
explain economic models for optimization purposes
explain economic models for optimization purposes
define the principle of simplex methods
define the principle of simplex methods
describe basic iterative methods of optimization
describe basic iterative methods of optimization
describe principles of game theory of two players
describe principles of game theory of two players
Skills
solve derivatives and partial derivatives of real functions
solve derivatives and partial derivatives of real functions
find unconstrained and constrained extrema of real functions
find unconstrained and constrained extrema of real functions
define and solve the simplex tableau
define and solve the simplex tableau
solve economic problems by linear and dynamic programming
solve economic problems by linear and dynamic programming
solve problems of matrix game theory of two players
solve problems of matrix game theory of two players
Recommended literature


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester