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Last update: T_KPMS (06.05.2014)
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Last update: T_KPMS (06.05.2014)
Lecture builds up base of modern nonconvex optimization and development of stability in stochastic programming. The theory is applied to particular stochastic optimization problems. |
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Last update: doc. RNDr. Petr Lachout, CSc. (11.10.2017)
The course is finalized by exam. |
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Last update: T_KPMS (06.05.2014)
[1] Bonnans, J. F.; Shapiro, A.: Perturbation Analysis of Optimization Problems. Springer-Verlag, New York, 2000. [2] Rockafellar, R.T.; Wets, R. J.-B.: Variational Analysis, Springer, Berlin 1998. [3] Ruszczyński, A.; Shapiro, A; Eds.: "Stochastic Programming. Handbooks in OR & MS, volume 10,". Elsevier, Amsterdam, 2003. [4] Shapiro, A.; Dentcheva, D.; Ruszczyński, A.: "Lectures on Stochastic Programming: Modeling and Theory". MPS-SIAM, Philadelphia, 2009. |
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Last update: T_KPMS (06.05.2014)
Lecture. |
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Last update: doc. RNDr. Petr Lachout, CSc. (14.02.2024)
+--------------------------------------------------------------------------- Requirements to exam +--------------------------------------------------------------------------- The exam is oral. Examination is checking knowledge of all topics read at the lecture and parts given to self-study by the course lecturer.
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Last update: T_KPMS (06.05.2014)
Variational Analysis 1) Convex analysis in finite dimension. 2) Cones and cosmic closure. 3) Set convergence. 4) Set-valued mappings. 5) Epi-convergence. 6) Variation analysis. 7) Subgradient and subdiferential. 8) Lipschitz properties. 9) Legendre-Fenchel duality.
Sensitivity of stochastic programming 1) Stability in stochastic programming. 2) Methods of parametric optimization. Probabilistic metrics. 3) Methods of asymptotic and robust statistics. |
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Last update: doc. RNDr. Petr Lachout, CSc. (30.05.2018)
basic of optimization theory, convex analysis |