Optimization
Prerequisites: MATH 351 or permission from the Graduate Program Director.
The course is a rigorous treatment of linear, nonlinear, and integer optimization, and may include optimization on graphs, stochastic optimization, etc. Modeling of real-life problems as optimization problems, mathematical analysis of resulting optimization problems, including proving existence of solutions, optimality conditions, convergence of algorithms, and computational approaches to solving the problems will be covered.
Outcomes: Students will learn how to recognize optimization problems, model real-life challenges as optimization problems, solve the problems using computational methods, and perform rigorous mathematical analysis of the problems and prove convergence of the computational methods.
The course is a rigorous treatment of linear, nonlinear, and integer optimization, and may include optimization on graphs, stochastic optimization, etc. Modeling of real-life problems as optimization problems, mathematical analysis of resulting optimization problems, including proving existence of solutions, optimality conditions, convergence of algorithms, and computational approaches to solving the problems will be covered.
Outcomes: Students will learn how to recognize optimization problems, model real-life challenges as optimization problems, solve the problems using computational methods, and perform rigorous mathematical analysis of the problems and prove convergence of the computational methods.