Coursebooks 2017-2018

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Optimization and simulation

MATH-600

Lecturer(s) :

Bierlaire Michel

Language:

English

Remarque

Every year/ Next time: Spring 2018

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

Resources

Bibliography

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

Ross S. (2013) Simulation, Elsevier

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