Times

I have offered this class in the fall of 2015 and spring of 2018, and am going to teach it again in the academic year 2021-22.

Description

This course provides students with an overview of state-of-the-art numerical methods for solving both unconstrained and constrained, large-scale optimization problems. Algorithm analysis and development will be emphasized, including efficient and robust implementations.

In addition, students will be exposed to state-of-the-art software that can be used to solve optimization problems.

Literature

The main references are

• Numerical Optimization by J. Nocedal and S.J. Wright
• Introduction to Nonlinear Optimization by A. Beck
• Convex Optimization by S. Boyd and L. Vandenberghe