Fiches de cours

Optimization and simulation

MATH-600

Lecturer(s) :

Bierlaire Michel

Language:

English

Frequency

Every year

Remarque

Every year/ Next time: Spring 2020

Summary

Master state-of-the art methods in discrete optimization and simulation. Work involves: - reading the material beforehand - class hours to discuss the material and solve problems - homework

Content

Part 1: Simulation

Sheldon M. Ross (1997) Simulation

Draws (Chapters 4 & 5)

Discrete event simulation (Chapter 6)

Statistical data analysis, bootstrapping (Chapter 7) 

Variance reduction techniques (Chapter 8) 

Markov Chain Monte Carlo methods (Chapter 10)

 

Part 2: Optimization:

heuristics Bierlaire M. (2015) Optimization: principle and algorithms  Classical optimization problems (chapter 25) 

Greedy heuristics (section 27.1) 

Neighborhood ansd local search (section 27.2) 

Diversification (sections 27.3 and 27.4)

Note

5 weeks on nonlinear optimization + 8 weeks on simulation

Keywords

optimization, simulation

Learning Prerequisites

Required courses

Analysis, algebra, probability and statistics, Matlab or Octave

Supervision

Office hours Yes
Assistants Yes
Forum Yes

Resources

Bibliography

Bierlaire M. (2015) Optimization: principles and algorithms, EPFL Press

Ross S. (2013) Simulation, Elsevier

Ressources en bibliothèque
Websites
Moodle Link
Videos

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