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Coursebooks
Optimization and simulation
MATH-600
Lecturer(s) :
Bierlaire MichelLanguage:
English
Frequency
Every yearRemark
Every year/ Next time: Spring 2021Summary
Master state-of-the art methods in optimization with heuristics and simulation. Work involves: - reading the material beforehand - class hours to discuss the material and solve problems - homeworkContent
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 and local search (section 27.2)
Diversification (sections 27.3 and 27.4)
Keywords
optimization, simulation
Learning Prerequisites
Required courses
Analysis, algebra, probability and statistics, Python programming language
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
In the programs
- Semester
- Exam form
Multiple - Credits
4 - Subject examined
Optimization and simulation - Lecture
35 Hour(s) - Practical work
55 Hour(s)
- Semester
- Semester
- Exam form
Multiple - Credits
4 - Subject examined
Optimization and simulation - Lecture
35 Hour(s) - Practical work
55 Hour(s)
- Semester
- Semester
- Exam form
Multiple - Credits
4 - Subject examined
Optimization and simulation - Lecture
35 Hour(s) - Practical work
55 Hour(s)
- Semester
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