Subjects

Your browser does not support JavaScript, or its support is disabled. Some features may not be available.

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

Opinion survey results Examination dates Noticeboard

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)