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Last update: RNDr. Jan Hric (12.05.2022)
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Last update: RNDr. Jakub Bulín, Ph.D. (06.05.2024)
Understand the main principles of various heuristic optimization methods based on convex optimization and artificial intelligence, with emphasis on large-scale instances. Learn how to apply these methods in practice. |
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Last update: RNDr. Jakub Bulín, Ph.D. (13.05.2022)
Students are expected to implement practical homework assignments and pass theoretical examination. The nature of homework assignments excludes the possibility of repeated attempts to get credit. |
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Last update: RNDr. Jakub Bulín, Ph.D. (13.05.2022)
Wolsey, Laurence A. Integer programming. Vol. 42. New York: Wiley, 1998.
Kochenderfer, Mykel J., and Tim A. Wheeler. Algorithms for optimization. MIT Press, 2019.
Blum, Christian, and Günther R. Raidl. Hybrid Metaheuristics: Powerful Tools for Optimization. Springer, 2016.
Desaulniers, Guy, Jacques Desrosiers, and Marius M. Solomon, eds. Column generation. Vol. 5. Springer Science & Business Media, 2006. |
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Last update: RNDr. Jakub Bulín, Ph.D. (13.05.2022)
The course is taught bi-yearly, alternating with the course Large-scale optimization: Exact methods (NOPT059). |