MATH-513 / 5 credits

Teacher: Eisenbrand Friedrich

Language: English


Summary

The course aims to introduce the basic concepts and results on metric embeddings, or more precisely on approximate embeddings. This area has been under rapid development since the 90's and it has strong impact on algorithms for discrete optimization problems.

Content

Learning Prerequisites

Recommended courses

  • Linear algebra 1+2
  • Introduction to Algorithms or Discrete Optimization

Resources

Bibliography

Jiri Matousek: Lecture notes on metric embeddings

 

Ressources en bibliothèque

In the programs

  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Metric embeddings
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Metric embeddings
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Metric embeddings
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Metric embeddings
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9    ELG120
9-10    
10-11    ELG120
11-12    
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     

Friday, 8h - 10h: Lecture ELG120

Friday, 10h - 12h: Exercise, TP ELG120