Coursebooks 2017-2018

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Optimization Methods and Models

MGT-631

Lecturer(s) :

Kuhn Daniel

Language:

English

Remarque

13, 20, 27 october, 3, 10, 17, 24 november, 1, 8, 15 december 2017 from 08:15-12:00

Summary

This course introduces the theory and application of modern optimization from an engineering perspective.

Content

The following topics will tentatively be covered in the course:

  1. Introduction
  2. Convex Sets
  3. Convex Functions
  4. Convex Optimization Problems 
  5. Separation Theorems
  6. Duality
  7. Optimality Conditions
  8. Optimization in Statistics & Machine Learning 
  9. Convexifying Nonconvex Problems 
  10. Stochastic Programming
  11. Robust Optimization

Learning Prerequisites

Important concepts to start the course

Students are assumed to have good knowledge of basic linear algebra and analysis. Some familiarity with linear programming or other optimization paradigms is useful but not necessary.

Learning Outcomes

Teaching methods

Classical formal teaching interlaced with practical exercices.

Assessment methods

Resources

Bibliography

 

 

Ressources en bibliothèque

In the programs

Reference week

 
      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