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 2024 to 2024
Semester: summer
E-Credits: 5
Hours per week, examination: summer s.:2/2, C+Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: not taught
Language: Czech, English
Teaching methods: full-time
Additional information: https://jbulin.github.io/teaching/spring/nopt061/
Guarantor: RNDr. Jakub Bulín, Ph.D.
RNDr. Jiří Fink, Ph.D.
Mgr. Marika Ivanová, 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
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Annotation -
Lecture on heuristic optimization algorithms based on Convex Optimization and Artificial Intelligence for solving real-life problems.
Last update: Hric Jan, RNDr. (12.05.2022)
Aim of the course -

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. (The course is suitable for 3rd-year bachelor's students and for master's students.)

Last update: Bulín Jakub, RNDr., Ph.D. (06.05.2024)
Course completion requirements -

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.

Last update: Bulín Jakub, RNDr., Ph.D. (13.05.2022)
Literature -

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.

Last update: Bulín Jakub, RNDr., Ph.D. (13.05.2022)
Syllabus -
  • 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).

Last update: Bulín Jakub, RNDr., Ph.D. (13.05.2022)