UE Convex and distributed optimization

Diplômes intégrant cet élément pédagogique :

Descriptif

Courses: (4 parts of 3h each + 3 hours practical work)

  1. Introduction to convex optimization: concepts in convex analysis (duality, proximal operators), how to identify potential difficulties in optimization problems. Illustrations in supervised learning (classification and regression problems) and in operation research (decomposition methods).
  2. Algorithms in convex optimization (gradient, proximal gradient, conditional gradient, ADMM)
  3. Introduction to distributed computation (architectures for computation, map-reduce scheme, MPI, Spark) + 3h practical work
  4. Distributed optimisation algorithms, stochastic algorithms, asynchronous methods.

Practical work (2 parts of 6h each)

  1. application to a recommendation system
  2. sparse logistic regression in high dimension

Informations complémentaires

Méthode d'enseignement : En présence
Lieu(x) : Grenoble - Domaine universitaire
Langue(s) : Anglais