Credits:
Semesters Offered
Learning Objectives
Students will be introduced to linear, nonlinear, unconstrained, constrained optimization. Convex optimization will be highlighted. Applications will be considered, in particular in the area of machine learning. Some optimization algorithms may be discussed, time permitting. (Formerly ENEE469O)
Prerequisite: Minimum grade of C- in one of the following: ENEE290, MATH240, MATH341, or MATH461; and must have completed or be concurrently enrolled in ENEE324 or STAT400
Learning Outcomes
- Understand convex functions and convex sets.
- Formulate a suitable mathematical optimization problem when given a problem description.
- Identify whether or not an optimization problem is a convex optimization problem.
- Find the Lagrange dual function and formulate a Lagrange dual problem when given optimization problem.
- Use the Karush-Kuhn-Tucker conditions to verify an optimal point for optimization problems indicating strong duality.
- Understand the key ideas behind algorithms used to solve unconstrained and constrained optimization problems.