|
|
|
||
|
Last update: G_M (16.05.2012)
|
|
||
|
Last update: T_KPMS (25.04.2016)
The goal is to give explanation and theoretical background for standard optimization procedures. Students will learn necessary theory and practice their knowledge on numerical examples. |
|
||
|
Last update: doc. RNDr. Martin Branda, Ph.D. (28.04.2020)
The exercise class credit is necessary to sign up for the exam.
Requirements for exercise class credit: The credit for the exercise class will be awarded to the student who is present at the exercise class sessions (two absences are tolerated) and hands in a satisfactory solution to each of five standard assignments to get 80% of total points and at the same time hands in a satisfactory solution to an additional homework on simplex algorithm.
The nature of these requirements precludes any possibility of additional attempts to obtain the exercise class credit.
It is probable that a large part of the exams could take place in a distance form. It depends on a development of the situation and we will inform you about the changes immediately. |
|
||
|
Last update: doc. RNDr. Martin Branda, Ph.D. (28.10.2019)
Bazaraa, M.S.; Sherali, H.D.; Shetty, C.M.: Nonlinear programming: theory and algorithms. Wiley, New York, 1993. Bertsekas, D.P.: Nonlinear programming. Athena Scientific, Belmont, 1999. Dupačová, J., Lachout, P.: Úvod do optimalizace. MatfyzPress, Praha, 2011. (in Czech only) Rockafellar, T.: Convex Analysis. Springer-Verlag, Berlin, 1975. Wolsey, L.A.: Integer Programming, Wiley, New York, 1998. |
|
||
|
Last update: T_KPMS (15.05.2012)
Lecture+exercises. |
|
||
|
Last update: doc. RNDr. Martin Branda, Ph.D. (28.04.2020)
The exam is in the form of written test, which consists of three computational examples (solved during practicals). The theory discussed during the lectures is part of the examples. It is necessary to get 60% of total points to pass.
It is probable that a large part of the exams could take place in a distance form. It depends on a development of the situation and we will inform you about the changes immediately. |
|
||
|
Last update: T_KPMS (25.04.2016)
1. Optimization problems and their formulations. Applications in economics, finance, logistics and mathematical statistics. 2. Basic parts of convex analysis (convex sets, convex multivariate functions). 3. Linear Programming (structure of the set of feasible solutions, simplex algorithm, duality, Farkas theorem). 4. Integer Linear Programming (applications, branch-and-bound algorithm). 5. Nonlinear Programming (local and global optimality conditions, constraint qualifications). 6. Quadratic Programming as a particular case of nonlinear programming problem.
|