IGFE MAT 7006
Master of Science
Acquiring some notions of optimization in continuous, discrete or mixed spaces and their relationship with concrete applications.
Basic Calculus, Basic Algebra
- 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.
- J.Dréo, A.Petrowski, P.Siarry, E.Taillard, Metaheuristics for hard optimization, Mathematical Methods for Operations Research, vol. 66, N°3, December 2007, Springer Berlin.
- L.Rabiner, B.H. Juang, Fundamentals of Speech Recognition, Prentice Hall Signal Processing Series, 1993.
- S.Haykin, Neural Networks : a comprehensive founda j