Introduction to optimization

Objectives

Learn the basics of optimization methods: decision variables, objective function, minimization of nonlinear problems, least squares problems, minimization under stress

   numerical optimization approach: iterative gradient methods; least squares problems; other numerical methods such as simulated annealing; network / graph problems

Description

1. Free and constrained minimization, Lagrange multipliers, convexity

2. Application 1: Nonlinear Regression, Model Registration,

3. Application 2: Newton's method for finding equilibrium points

4. Functional optimization

5. Application: minimal surfaces

Targeted skills

- be able to pose an optimization problem with or without constraint

- be able to use solvers (Matlab, Python ...) to solve minimization problems, linear / nonlinear regression type, Newton's method ...

- be able to apply functional minimization and Euler-Lagrange equations for simple systems

Session 1 ou session unique - Contrôle des connaissances

Session 2 - Contrôle des connaissances