This course aims at providing the essential concepts and techniques for optimization problems. Topics include unconstrained optimization, gradient-based methods, constrained optimization, simplex method, quadratic programming, nonlinear optimization, Lagrange multipliers, penalty method, Karush-Kuhn-Tucker conditions. More advanced topics such as Markov chain, optimal control, Monte Carlo Methods and Metaheuristic algorithms (genetic algorithms, simulated annealing, harmony search) are also included.

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