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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 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)