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Optimization problems arise in various fields ranging from economics to physics and in many aspects of our daily lives. For example, airlines arrange their schedules to maximize their profit subject to constraints imposed by limited resources such as the number of crew-members and planes. Ray of light follows a path to minimize the travel time. Two main ingredients of an optimization problem are: an objective function which we want to minimize or maximize, and a set of constraints which determines the set of allowable points over which the objective function must be minimized or maximized. The main purpose of this course is to learn an algorithmic approach to continuous numerical optimization, constrained and unconstrained. Emphasis on practical methods with enough practical examples and enough theory to make them work.