Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics

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    Advanced Mathematics for Electrical Engineers - Combinatorial Optimization

    A tantárgy neve magyarul / Name of the subject in Hungarian: Felsőbb matematika villamosmérnököknek - Kombinatorikus optimalizálás

    Last updated: 2016. január 4.

    Budapest University of Technology and Economics
    Faculty of Electrical Engineering and Informatics
    MSc degree program in Electrical Engineering

    Course ID Semester Assessment Credit Tantárgyfélév
    VISZMA06 1 2/1/0/f 3  
    3. Course coordinator and department Dr. Szeszlér Dávid,
    Web page of the course http://cs.bme.hu/combopt/
    4. Instructors

    Dr. Rita Csákány, associate professor, Department of Computer Science and Information Theory

    Dr. Tamás Fleiner, associate professor, Department of Computer Science and Information Theory

    Dr. Dávid Szeszlér, associate professor, Department of Computer Science and Information Theory
    5. Required knowledge basics of linear algebra, graph theory and algorithms
    6. Pre-requisites
    Kötelező:
    NEM ( TárgyEredmény( "BMETE90MX38" , "jegy" , _ ) >= 2
    VAGY
    TárgyEredmény("BMETTE90MX38", "FELVETEL", AktualisFelev()) > 0)

    A fenti forma a Neptun sajátja, ezen technikai okokból nem változtattunk.

    A kötelező előtanulmányi rendek grafikus formában itt láthatók.

    7. Objectives, learning outcomes and obtained knowledge The subject introduces some areas of operations research and combinatorial optimization. Besides covering the most relevant algorithms and methods and their limits, it also aims at giving a glimpse into some of their engineering applications. Thus the subject also covers some general algorithmic approaches like linear and integer programming and matroid theory. Furthermore, the course aims at extending and deepening the knowledge formerly provided be the Foundations of Computer Science subject  of the BSc degree program in Electrical Engineering.
    9. Method of instruction 2 hours of lecture and 1 hour of problem solving per week
    10. Assessment There are three midterm tests during the semester. The condition of completing the subject is a result of at least 40% on all three midterm tests. If this condition is met then the final result is computed from the average of the three midterms. 
    11. Recaps The opportunity to retake all three midterms (either to replace an unsuccessful or missed midterm or to improve the result of a successful one) will be provided during the semester. Besides that, there will be a further chance to retake one unsuccessful or missed test in the week preceding the exam period.
    12. Consultations Subject to individual arrangements.
    13. References, textbooks and resources Foulds, L. R. (2012). Combinatorial optimization for undergraduates. Springer Science & Business Media.
    14. Required learning hours and assignment
    In class 42
    Preparation for classes 9
    Preparation for midterms 39
    Homework 
    Reading assignment 
    Preparation for final 
    Total 90
    15. Syllabus prepared by Dr. Dávid Szeszlér, associate professor, Department of Computer Science and Information Theory