IGFE MAT 7006
Master of Science
Acquiring some notions of optimization in continuous, discrete or mixed spaces and their relationship with concrete applications.
- Dynamic programming
- Branch and Bound methods
- B&B and the Travelling Salesman problem : the Little algorithm
- Linear Programming : the simplex algorithm
- Unconstrained non-linear Programming : gradient methods, Newton method, quasi-Newton methods
- Metaheuristics for hard optimization : Taboo Search, Evolutionary Computation, Simulated Annealing
- Applications to Pattern Recognition : elastic distance, Dynamic Time Warping, gradient methods in neural networks, etc.
Basic Calculus, Basic Algebra