ENGG 5501: Foundations of Optimization
2017-18 First Term
In this course we will develop the basic machinery for formulating and analyzing various optimization problems. Topics include convex analysis, linear and conic linear programming, nonlinear programming, optimality conditions, Lagrangian duality theory, and basics of optimization algorithms. Applications from different fields, such as combinatorial optimization, communications, computational economics and finance, machine learning, and signal and image processing, will be used to complement the theoretical developments. No prior optimization background is required for this class. However, students should have workable knowledge in multivariable calculus, real analysis, linear algebra and matrix theory.
Homework sets (60%) and an in-class final examination (40%).
The mathematical prerequisites for this course are summarized in Handouts B and C. Students are strongly advised to go through them carefully.
Homework Sets (Assignment Box: A17, 5th floor of ERB)
Last Updated: November 14, 2017