Due to the COVID19 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 nonsmooth convex optimization.
 Interiorpoint methods.
Aims
At the end of this learning unit, the student is able to :  
1 
Learning outcomes:

Content
 General problem of nonlinear optimization. Blackbox concept. Iterative methods and analytical complexity. Gradient method and Newton method. Local complexity analysis.
 Convex optimization: convex sets and functions; minimization of differentiable and nondifferentiable convex functions; lower complexity bounds; optimal methods.
 Interiorpoint methods: notion of selfconcordant functions and barriers; pathfollowing methods; structural optimization.
Teaching methods
Due to the COVID19 crisis, the information in this section is particularly likely to change.
The course is given in 1215 lectures. The computer projects are implemented by the students themselves with supporting consultations.
Evaluation methods
Due to the COVID19 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, « Interiorpoint polynomial algorithms in nonlinear optimization », SIAM, Philadelphia, 1994.
Faculty or entity
MAP