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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
Teaching methods:	full-time
Additional information:	https://ktiml.mff.cuni.cz/~fink/teaching/applications/

Guarantor:	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 -

Last update: RNDr. Jan Hric (03.05.2018)

An independent or a group project which uses artificial intelligence or mathematical optimization techniques to solve practical problems.

Aim of the course -

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

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.

Course completion requirements -

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

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

Literature -

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

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).

Syllabus -

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

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