Due to the COVID-19 crisis, the information below is subject to change,
in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
5 credits
30.0 h + 22.5 h
Q2
Teacher(s)
Nesterov Yurii;
Language
English
Main themes
- General nonlinear optimization.
- Smooth and non-smooth convex optimization.
- Interior-point methods.
Aims
At the end of this learning unit, the student is able to : | |
| 1 |
Learning outcomes:
|
Content
- General problem of nonlinear optimization. Black-box concept. Iterative methods and analytical complexity. Gradient method and Newton method. Local complexity analysis.
- Convex optimization: convex sets and functions; minimization of differentiable and non-differentiable convex functions; lower complexity bounds; optimal methods.
- Interior-point methods: notion of self-concordant functions and barriers; path-following methods; structural optimization.
Teaching methods
Due to the COVID-19 crisis, the information in this section is particularly likely to change.
The course is given in 12-15 lectures. The computer projects are implemented by the students themselves with supporting consultations.
Evaluation methods
Due to the COVID-19 crisis, the information in this section is particularly likely to change.
In the written exam (in English or French) there are four questions, one for each chapter of the course (up to 5 points for each question). The marks for the exam and the exercises are combined in the final mark.
Online resources
The full syllabus (in English) can be downloaded from the web page of the course.
Bibliography
- Yu.Nesterov. "Introductory lectures on convex optimization. Basic course", Kluwer 2004
- P. Polyak, « Introduction in optimization », J. Willey & Sons, 1989
- Yu. Nesterov, A. Nemirovsky, « Interior-point polynomial algorithms in nonlinear optimization », SIAM, Philadelphia, 1994.
Faculty or entity
MAP
