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Lecturer(s)
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Valdman Jan, doc. Dr.rer.nat. Ing. DSc.
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Klicnarová Jana, doc. RNDr. Ph.D.
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Course content
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Lecture contents: 1. - 2. Vector calculus: function of several variables, partial derivatives, gradients, high order derivatives, Taylor series 3. - 4. Optimization: gradient descent, constrained optimization, Lagrange multipliers 5. - 6. Optimalizace: convex optimization, linear and quadratic programming 7. - 8. Networking Optimization Models 9. - 10. Dynamic Programming 11. Nonlinear Programming 12. - 13.Multiple Criteria Programming Tutorial contents: Solution of optimization problems from the field of natural sciences and economics.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Work with text (with textbook, with book)
- Preparation for classes
- 28 hours per semester
- Preparation for credit
- 28 hours per semester
- Preparation for exam
- 28 hours per semester
- Class attendance
- 28 hours per semester
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Learning outcomes
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Basic principles of optimization with applications
the principle of optimality in practice
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Prerequisites
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UMB 010 Matematika
UMB/551 and UMB/564
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Assessment methods and criteria
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Written examination, Interim evaluation
To complete the course it is necessary to pass the credit and pass the exam. To gain credit, sufficient attendance at the exercises and sufficient average success rate (55%) in short practice tests. Credit is a prerequisite for the exam test. The exam is written and is successful in reaching 55% of the test.
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Recommended literature
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Corriou J.-P. Numerical Methods and Optimization. Theory and Practice for Engineers (Springer Optimization and Its Applications Book 187), Springer 2021..
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Deisenroth M. P., A. A. Faisal, Cheng Soon Ong. Mathematics for Machine Learning, Cambridge University Press; 1st edition 2020.
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Dostál, Z. a P. Beremlijski. Metody optimalizace, VŠB TU Ostrava (3. vydání), 2023.
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Hillier, F. S. and Lieberman, G. J. (2013). Introduction to Operations Research. New York: McGraw-Hill..
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