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Course, academic year 2022/2023
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Large-scale optimization: Metaheuristics - NOPT061
Title: Optimalizace velkých problémů: metaheuristiky
Guaranteed by: Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Faculty: Faculty of Mathematics and Physics
Actual: from 2022 to 2022
Semester: summer
E-Credits: 5
Hours per week, examination: summer s.:2/2, C+Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
Virtual mobility / capacity: no
State of the course: not taught
Language: Czech, English
Teaching methods: full-time
Additional information:
Guarantor: Mgr. Marika Ivanová, Ph.D.
RNDr. Jakub Bulín, Ph.D.
RNDr. Jiří Fink, Ph.D.
Class: Informatika Mgr. - Teoretická informatika
Classification: Informatics > Informatics, Software Applications, Computer Graphics and Geometry, Database Systems, Didactics of Informatics, Discrete Mathematics, External Subjects, General Subjects, Computer and Formal Linguistics, Optimalization, Programming, Software Engineering, Theoretical Computer Science, Optimalization
Annotation -
Last update: RNDr. Jan Hric (12.05.2022)
Lecture on heuristic optimization algorithms based on Convex Optimization and Artificial Intelligence for solving real-life problems.
Aim of the course -
Last update: RNDr. Jakub Bulín, Ph.D. (13.05.2022)

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.

Course completion requirements -
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.

Literature -
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.

Syllabus -
Last update: RNDr. Jakub Bulín, Ph.D. (13.05.2022)
  • Local search, Hill climbing, Simulated annealing
  • Population methods, e.g. Genetic algorithms
  • Problem instance reduction, Large neighborhood search
  • Hybrid methods: Lamarckian vs. Baldwinian learning, examples
  • Surrogate models
  • Applications, e.g. Minimum Common String Partition, Minimum Weight Dominating Set Problem, Arc Routing Problems, Public Transportation

The course is taught bi-yearly, alternating with the course Large-scale optimization: Exact methods (NOPT059).

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