Convex optimization

Learn the basics of mathematical optimization and its applications.

This UE will cover the following points:

      - Unconstrained extrema: notion of convexity, optimality conditions, descent methods, separable functionals, stochastic gradient

      - Extremums with constraints: strong and weak formulation, linked extrema, Lagrange multipliers and implementation with Newton. KKT conditions, duality, Uzawa. Linear programming

      - Introduction to Mathematical Learning

      - Some areas of application:

Analysis courses in L1, L2 and the first semester of L3, in particular :

- HAX404X Topology of R ⁿ and functions of several variables

- HAX502X Differential calculus and differential equations

 

 

Recommended prerequisites: first semester of L3

Hourly volumes :

            CM: 18

            TD : 15

            TP: 12

            Land: -