| Course Unit Code | 330-0541/01 |
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| Number of ECTS Credits Allocated | 5 ECTS credits |
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| Type of Course Unit * | Compulsory |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | First Year |
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| Semester when the Course Unit is delivered | Summer Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | Czech |
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| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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| Name of Lecturer(s) | Personal ID | Name |
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| ZAP60 | prof. Ing. Jaroslav Zapoměl, DrSc. |
| MAW007 | doc. Ing. Pavel Maršálek, Ph.D. |
| Summary |
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| Students will learn the basic methods of local and global optimization, both in terms of theoretical foundations of each method, and also from the practical experiences, ie. software processing techniques in MATLAB. The content of this subject are methods based on optimization of smooth functions of one variable and functions of several variables, broken down into methods 0th, 1st and higher-order methods. Then follow the methods of global optimization like Monte Carlo method and methods derived from the genetic algorithm. Finally, students learn about the most common indication, practices in commercial software, this practical component is conducted in the ANSYS environment. |
| Learning Outcomes of the Course Unit |
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1. To characterize the basic types of optimization problems and recognize the role of problems in engineering practice.
2. Explain the principles of optimization methods, describe their algorithms and discuss their advantages and disadvantages.
3. Apply theoretical knowledge to solving practical problems, interpret the results, modify the solution procedure.
4. Analyze and evaluate the results obtained from optimization solutions, partly analyze the optimization procedures. |
| Course Contents |
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1. Optimization - Introduction: the practical use and mathematical terminology and basics.
2. Unconstrain optimization of continuous functions - the existence of optimum conditions.
3. Optimization of one variable function: the method of elimination and approximation; 0th and 1st order methods.
4. Optimization of multivariablefunction; 0th, 1st and higher order methods.
5. Introduction to global optimization.
6. Global optimization methods derived from Monte Carlo method and/or Genetic Algorithm method.
7. Optimization procedures available in the application software.
8. Solving optimization problems in the application software (now ANSYS and ANSYS Workbench). |
| Recommended or Required Reading |
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| Required Reading: |
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[1] Ravindran, A., Ragsdell, K. M.; Reklaitis, G. V. Engineering Optimization. 2nd ed. Wiley, 2003.
[2] Yang, Won-Yong, Cao, Wenwu, Chung, Tae-Sang, Morris, John. Applied Numerical Methods Using MATLAB®. Wiley, 2005. |
[1] Ravindran, A., Ragsdell, K. M.; Reklaitis, G. V. Engineering Optimization. 2nd ed. Wiley, 2003.
[2] Yang, Won-Yong, Cao, Wenwu, Chung, Tae-Sang, Morris, John. Applied Numerical Methods Using MATLAB®. Wiley, 2005. |
| Recommended Reading: |
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| [1] Lyshevski, Sergey E. Engineering and Scientific Computations Using MATLAB®. Wiley, 2003. |
| [1] Lyshevski, Sergey E. Engineering and Scientific Computations Using MATLAB®. Wiley, 2003. |
| Planned learning activities and teaching methods |
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| Lectures, Tutorials |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Graded credit | Graded credit | 100 | 51 |