Introduction to Optimization

Optimization holds an important place in both practical and theoretical worlds, as understanding the timing and magnitude of actions to be carried out helps achieve a goal in the best possible way.

This course emphasizes data-driven modeling, theory and numerical algorithms for optimization with real variables. Explore the study of maximization and minimization of mathematical functions and the role of prices, duality, optimality conditions, and algorithms in finding and recognizing solutions. Learn about applications in machine learning, operations, marketing, finance and economics.

Topics Include

Perspectives: problem formulation, analytical theory, computational methods, and recent applications in engineering, finance, and economics
Theories: finite dimensional derivatives, convexity, optimality, duality, and sensitivity
Methods: simplex and interior-point, gradient, Newton, and barrier