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 20Littérale/grade réduitPour les étudiants du diplôme M2 Data Science
Le rattrapage est autorisé (Max entre les deux notes)- Crédits ECTS acquis : 3 ECTS