Introduction à Optimisation

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

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