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Fiches de cours 2017-2018
Optimization Methods and Models
MGT-631
Lecturer(s) :
Kuhn DanielLanguage:
English
Remarque
13, 20, 27 october, 3, 10, 17, 24 november, 1, 8, 15 december 2017 from 08:15-12:00Summary
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:
- Introduction
- Convex Sets
- Convex Functions
- Convex Optimization Problems
- Separation Theorems
- Duality
- Optimality Conditions
- Optimization in Statistics & Machine Learning
- Convexifying Nonconvex Problems
- Stochastic Programming
- 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
- Formalize decision problems in management science and engineering as mathematical optimization models
- Solve the resulting models with commonly used optimization software and to interpret the results
- Assess / Evaluate the computational complexity of different classes of optimization problems and use modeling techniques to make specific optimization problems more tractable
- Model and solve decision problems affected by uncertainty
Teaching methods
Classical formal teaching interlaced with practical exercices.
Assessment methods
- Participation in class
- Final exam
Resources
Bibliography
- Stephen Boyd and Lieven Vandenberghe, Convex Optimization, Cambridge University Press, 2004
- Aharon Ben-Tal and Arkadi Nemirovski, Lectures on Modern Convex Optimization, SIAM, 2001
- Yurii Nesterov, Introductory Lectures on Convex Optimization: A Basic Course, Springer, 2004
- David Luenberger and Yinyu Ye, Linear and Nonlinear Programming, Springer, 2008
- R. Tyrrell Rockafellar, Conjugate Duality and Optimization, SIAM, 1974
Ressources en bibliothèque
In the programs
- Semester
- Exam form
Multiple - Credits
4 - Subject examined
Optimization Methods and Models - Lecture
56 Hour(s)
- Semester
- Semester
- Exam form
Multiple - Credits
4 - Subject examined
Optimization Methods and Models - Lecture
56 Hour(s)
- Semester
- Semester
- Exam form
Multiple - Credits
4 - Subject examined
Optimization Methods and Models - Lecture
56 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