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Lecturer(s)
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Tomeček Jan, doc. RNDr. Ph.D.
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Course content
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1. Unconstrained optimization. 2. Newton-like methods. 3. The Conjugate gradient method. 4. Constrained optimization. 5. Lagrange multipliers. 6. First and second order conditions. 7. Convexity and duality. 8. Linear, quadratic and nonlinear programming. 9. Nonsmooth optimization. 10. Generalized derivatives. 11. Vector optimization.
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Learning activities and teaching methods
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Lecture, Work with Text (with Book, Textbook)
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Learning outcomes
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Apply differential and generalized differential calculus of functions of several variables to smooth and also nonsmooth scalar and vector optimization.
Application Apply differential and generalized differential calculus of functions of several variables to smooth and also nonsmooth scalar and vector optimization.
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Prerequisites
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Master's degree in mathematics.
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Assessment methods and criteria
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Oral exam, Dialog
Exam: the student has to understand the subject and be able to prove the principal results.
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Recommended literature
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B.S. Mordukhovich. (2006). Variational Analysis and Generalized Differentiation I, II. Springer, New York.
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J. Jahn. (2004). Vector Optimization: Theory, Applications and Extensions. Springer, Berlin.
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R. Fletcher. (1991). Practical methods of optimization. John Wiley & Sons.
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