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Cours scientifiques - DS-ENSTA-1 : Optimisation sous-différentiable et méthodes proximales

Domaine > Mathématiques appliquées.

Descriptif

Cooperative Optimization for Data Science


The course presents continuous optimization techniques that have been developed to deal with
the increasing amount of data. In particular, we look at optimization problems that depend on
large-scale datasets, spatially distributed data, as well as local private data.

We will focus on three different aspects: (1) the development of algorithms to decompose the
problem into smaller problems that can be solved with some degree of coordination; (2) the trade-
off of cooperation vs. local computation; (3) how to design algorithms that ensure privacy of
sensitive data.

Diplôme(s) concerné(s)

Pour les étudiants du diplôme M2 Data Science

Requirements.
-
Previous course on convex optimization, especially first-order algorithms (gradient descent),
optimality conditions (KKT), and duality.

Format des notes

Numérique sur 20

Littérale/grade réduit

Pour les étudiants du diplôme M2 Data Science

Le rattrapage est autorisé (Max entre les deux notes)
    L'UE est acquise si Note finale >= 10
    • Crédits ECTS acquis : 3 ECTS
    Veuillez patienter