Applications of optimization techniques - NOPT058
Title: Aplikace optimalizačních technik
Guaranteed by: Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Faculty: Faculty of Mathematics and Physics
Actual: from 2018
Semester: summer
E-Credits: 3
Hours per week, examination: summer s.:0/2, C [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: taught
Language: Czech
Teaching methods: full-time
Additional information: https://ktiml.mff.cuni.cz/~fink/teaching/applications/
Guarantor: RNDr. Jiří Fink, Ph.D.
Teacher(s): RNDr. Jiří Fink, Ph.D.
Class: Informatika Mgr. - volitelný
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
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Annotation -
An independent or a group project which uses artificial intelligence or mathematical optimization techniques to solve practical problems.
Last update: Hric Jan, RNDr. (03.05.2018)
Aim of the course -

The aim of the course is teach students to use methods of artificial intelligence and mathematical optimization in practical problems. Students should learn to:

  • describe a given problem using mathematical tools,
  • obtain real testing data from publicly available sources or generate realistic data,
  • choose an appropriate artificial intelligence and mathematical optimization tool for a given problem,
  • analyze results of simulations,
  • write a report describing the studied problem, methodology and results.
Last update: Fink Jiří, RNDr., Ph.D. (03.05.2018)
Course completion requirements -

An independent or a group project fulfilling goals of the course.

Last update: Fink Jiří, RNDr., Ph.D. (01.05.2018)
Literature -

Beale R. and Jackson T.: Neural Computing: An Introduction, IOP Publishing, Bristol and Philadelphia, 1990

Mitchell, M.: Introduction to genetic algorithms. MIT Press, 1996.

W. Saad, Z. Han, H. V. Poor and T. Basar, "Game-Theoretic Methods for the Smart Grid: An Overview of Microgrid Systems, Demand-Side Management, and Smart Grid Communications," in IEEE Signal Processing Magazine, vol. 29, no. 5, pp. 86-105, Sept. 2012.

K. Dvijotham, P. Van Hentenryck, M. Chertkov, M. Vuffray, S. Misra, Graphical Models for Optimal Power Flow, Proceedings of 22nd International Conference on Principles and Practice of Constraint Programming (CP 2016).

Last update: Fink Jiří, RNDr., Ph.D. (01.05.2018)
Syllabus -

An independent or a group project which uses artificial intelligence or mathematical optimization techniques to solve practical problems. Examples of fields for studied projects are:

  • Smart grids: Prediction and optimization of the production, transportation and consumption of electricity in transmission and distribution networks, house heating (HVAC)
  • Logistics: Planning and optimization of transportation of people or goods
  • Scheduling

Last update: Fink Jiří, RNDr., Ph.D. (03.05.2018)