Coursebooks 2017-2018

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Caution, these contents corresponds to the coursebooks of last year


Convex optimization and applications

CS-454

Lecturer(s) :

Lebret Hervé

Language:

English

Summary

Optimization is not only a major segment of applied mathematics, it is also a critical problem in many engineering and economic fields. In any situation where resources are limited, decision makers try to solve problems they face in the best possible manner. The course provides theory and practice.

Content

The class will cover topics such as:
Convex sets and functions
Recognizing convex optimization problems
Optimality Conditions and Duality
Linear Programming (geometry of linear programming, applications in network optimization, the simplex method)
Least squares and quadratic programs
Semidefinite programming
Interior point methods

Keywords

Convex Optimisation

Learning Prerequisites

Required courses

A good background in linear algebra. Mastering MATLAB is a plus!

Recommended courses

Basic Linear Algebra

Learning Outcomes

By the end of the course, the student must be able to:

Teaching methods

Ex-cathedra lectures and exercise sessions(in English).

Assessment methods

Midterm (25%) and final exam (50%). Small personal project (25%). Exams are open-text and on paper (no use of computers)

Resources

Bibliography

Book : Convex Optimization by Stephen Boyd and Lieven Vandenberghe

Ressources en bibliothèque

In the programs

  • Data Science, 2017-2018, Master semester 2
    • Semester
      Spring
    • Exam form
      During the semester
    • Credits
      4
    • Subject examined
      Convex optimization and applications
    • Lecture
      1 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Computer Science, 2017-2018, Master semester 2
    • Semester
      Spring
    • Exam form
      During the semester
    • Credits
      4
    • Subject examined
      Convex optimization and applications
    • Lecture
      1 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Computational science and Engineering, 2017-2018, Master semester 2
    • Semester
      Spring
    • Exam form
      During the semester
    • Credits
      4
    • Subject examined
      Convex optimization and applications
    • Lecture
      1 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Communication Systems - master program, 2017-2018, Master semester 2
    • Semester
      Spring
    • Exam form
      During the semester
    • Credits
      4
    • Subject examined
      Convex optimization and applications
    • Lecture
      1 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Communication Systems - master program, 2017-2018, Master semester 4
    • Semester
      Spring
    • Exam form
      During the semester
    • Credits
      4
    • Subject examined
      Convex optimization and applications
    • Lecture
      1 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Systems Engineering minor, 2017-2018, Spring semester
    • Semester
      Spring
    • Exam form
      During the semester
    • Credits
      4
    • Subject examined
      Convex optimization and applications
    • Lecture
      1 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks

Reference week

MoTuWeThFr
8-9
9-10
10-11
11-12
12-13
13-14
14-15
15-16
16-17
17-18
18-19
19-20
20-21
21-22
Under construction
Lecture
Exercise, TP
Project, other

legend

  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German