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Course, academic year 2017/2018
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Advanced seminar on Optimization methods - NOPT058
English title: Pokročilý seminář z optimalizačních method
Guaranteed by: Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Faculty: Faculty of Mathematics and Physics
Actual: from 2016
Semester: summer
E-Credits: 2
Hours per week, examination: summer s.:0/1 C [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: Czech
Teaching methods: full-time
Additional information:
Guarantor: RNDr. Jiří Fink, Ph.D.
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
Annotation -
Last update: T_KTI (26.04.2016)

Students develop and implement efficient algorithms solving given optimization problems using existing theoretical (e.g. Linear Programming) and software (e.g. glpk a cplex) tools.
Aim of the course -
Last update: T_KTI (26.04.2016)

In this course, students practice using various software tools for solving optimization problems (e.g. MathProg, glpk, cplex) and learn to efficiently use them to implement fast algorithms.

Course completion requirements -
Last update: T_KTI (26.04.2016)

Students have to write problems solving given optimization problems and descriptions of implemented algorithms with proves of their correctness. The main criteria is the running time of programs on testing data.

Literature -
Last update: T_KTI (26.04.2016)

W.J.Cook, W.H. Cunningham, W.R.Pulleyblank, A. Schrijver, Combinatorial Optimization, John Wiley, 1997

J. Matoušek, B. Gärtner, Understanding and using linear programming, Springer, 2006.

GNU Linear Programming Kit, Reference Manual, 2014

IBM ILOG CPLEX V12.1 User's Manual for CPLEX, 2009

Syllabus -
Last update: T_KTI (26.04.2016)

The current languages used for describing optimization problems (e.g. MathProg) and programs for their solving.

Fast methods for finding optimal solutions.

Developing and profiling tools.

Practical implementations on given problems.

The course Optimization methods is a recommended co-requisite.

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