Mathematical Economics 2
Code | Completion | Credits | Range | Language |
---|---|---|---|---|
818ME2 | Z,ZK | 5 | 2+2 | Czech |
- Garant předmětu:
- Lecturer:
- Tutor:
- Supervisor:
- Department of Software Engineering
- Synopsis:
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The aim of this course is to provide students with a knowledge of the selected models for economics decision. We focus on dynamic programming, queuing theory, solution of linear and nonlinear models.
- Requirements:
- Syllabus of lectures:
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1. Dynamic programming: problem of optimal division of resources, problem of backpack
2. Dynamic programming: storage optimization
3. Dynamic programming: device recovery issues
4. Stochastic models of economic processes: recovery models
5. Mass operation models: introduction, classification, possibilities of use
6. Mass handling models: M/M/1 and their applications
7. Mass handling models: multiplication and death processes, M/M/C and their applications
8. Multi-criteria decision making: basic concepts and methods
9. Basic concepts of problem solving LP: graphical solution, simplex method-principle
10. Possibilities of solving LP problems in Excel
11. Solution of nonlinear problems: basic principles and concepts
12. Solution of nonlinear problems: one-dimensional optimization, golden section method, quadratic interpolation method
13. Solution of nonlinear problems: gradient method with long and short step
- Syllabus of tutorials:
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The structure of exercises is identical to lectures. Exercises are focused on typical problems from each theme.
Dynamic programming: resource allocation, knapsack problem
Dynamic programming: production scheduling
Dynamic programming: device recovery
Stochastic models: Markov chains
Queuing theory: introduction, classification
Queuing theory: M/M/1 and application
Queuing theory: M/M/C and application
Multi-criteria decision: introduction
Introduction to linear programming: graphical solution, simplex method
Linear programming in Excel
Nonlinear models: introduction
Solution of nonlinear models in 1D
Solution of nonlinear models: gradient method
- Study Objective:
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Knowledge: The aim of this course is to provide students basic overview of methods for economics decision
Abilities: Students gain ability to select and use appropriate method for their decision making problems.
- Study materials:
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Mandatory reading:
[1] Taha, H. A.. Operations Research: An Introduction, 10e. London: Pearson, 2017.
[2] Rardin, R. L.. Optimization in Operations Research, 2e. London: Pearson, 2015.
Recommended reading:
[3] Pelikán, J., Chýna, V.. Kvantitativní management. Praha: VŠE, 2011.
[4] Griva, I., Nash, S. G., Sofer, A.. Linear and Nonlinear Optimization, 2e. Philadelphia: Society for Industrial and Applied Mathematics, 2009.
[5] Kořenář, V.. Stochastické procesy. Praha: VŠE, 2010.
- Note:
- Further information:
- No time-table has been prepared for this course
- The course is a part of the following study plans: