Coursebooks 2017-2018

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Discrete optimization

MATH-261

Lecturer(s) :

Eisenbrand Friedrich

Language:

English

Summary

This course is an introduction to linear and discrete optimization. We will discuss linear programming and combinatorial optimization problems like bipartite matchings, shortest paths and flows. Warning: This course is for mathematicians! Strong emphasis is put on formal mathematical proofs.

Content

Keywords

Linear Programming

Algorithms

Complexity

Graphs

Learning Prerequisites

Required courses

Linear Algebra

Discrete Mathematics or Discrete Structures

Important concepts to start the course

The student needs to be able to prove theorems

Learning Outcomes

By the end of the course, the student must be able to:

Transversal skills

Teaching methods

Ex cathedra lecture, exercises in the classroom and with a computer

Expected student activities

Attendance of lectures and exercises

Completion of exercises

Solving supplementary programs with the help of a computer

Assessment methods

Written exam during the exam session

Resources

Bibliography

Dimitris Bertsimas and John N. Tsitsiklis: Introduction to Linear Optimization, Athena Scientific

Alexander Schrijver: Theory of Linear and integer Programming, Wiley

Ressources en bibliothèque
Notes/Handbook

Lecture notes

In the programs

  • Chemistry, 2017-2018, Bachelor semester 6
    • Semester
      Spring
    • Exam form
      Written
    • Credits
      5
    • Subject examined
      Discrete optimization
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Electrical and Electronics Engineering, 2017-2018, Master semester 2
    • Semester
      Spring
    • Exam form
      Written
    • Credits
      5
    • Subject examined
      Discrete optimization
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Electrical and Electronics Engineering, 2017-2018, Master semester 4
    • Semester
      Spring
    • Exam form
      Written
    • Credits
      5
    • Subject examined
      Discrete optimization
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Mathematics, 2017-2018, Bachelor semester 4
    • Semester
      Spring
    • Exam form
      Written
    • Credits
      5
    • Subject examined
      Discrete optimization
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks

Reference week

MoTuWeThFr
8-9
9-10
10-11
11-12
12-13
13-14
14-15
15-16
16-17
17-18
18-19
19-20
20-21
21-22
Under construction
Lecture
Exercise, TP
Project, other

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  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German