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Convex sets, convex functions. Elements of non-conditioned optimization. one-dimensional problems (line-search), methods of the type trust-region. Practical Newton's methods. Elements of conditioned optimization, optimality conditions. Quadratic programming, sequential quadratic programming. Methods of penalization and methods of an internal point for convex and non-convex conditioned optimization. Semidefinite programming.
Last update: G_M (19.06.2014)
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Ke zkoušce není nutný zápočet. Zápočet bude udělen za docházku. Charakter zápočtu neumožňuje opravné termíny. Last update: Kučera Václav, doc. RNDr., Ph.D. (14.06.2019)
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Literatura: J. Nocedal, S. Wright: Numerical Optimization. Springer, 1999. Last update: G_M (19.06.2014)
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Zkouška je ústní. Požadavky ke zkoušce odpovídají sylabu předmětu v rozsahu, který byl prezentován na přednášce. Last update: Kučera Václav, doc. RNDr., Ph.D. (14.06.2019)
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Convex sets, convex functions. Elements of non-conditioned optimization. one-dimensional problems (line-search), methods of the type trust-region. Practical Newton's methods. Elements of conditioned optimization, optimality conditions. Quadratic programming, sequential quadratic programming. Methods of penalization and methods of an internal point for convex and non-convex conditioned optimization. Semidefinite programming. Last update: G_M (19.06.2014)
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