3 Credit Hours

Introduction to methods for obtaining approximate solutions to unconstrained and constrained optimization problems of moderate size. Emphasis on geometrical interpretation and actual coordinate descent, steepest descent, Newton and quasi-Newton methods, conjugate gradient search, gradient projection and penalty function methods for constrained problems. Specialized problems and algorithms treated as time permits.

Prerequisites

Linear algebra or similar coursework (similar to MA 303, MA 405), and knowledge of a computer language, such as Python, MATLAB, Julia.

Course Objectives

Understanding of optimality conditions and different types of algorithms for unconstrained and constrained problems.

Course Outline

• Convex sets and functions
• Optimality conditions for unconstrained and constrained problems
• Numerical algorithms

Course Requirements

The course will require regular homework assignments and exams.

Textbook

Nonlinear Programming: Theory and Algorithms. Mokhtar S. Bazaraa, Hanif D. Sherali, C. M. Shetty. 3rd Edition. 2006. Wiley & Sons.

Created: 04/01/2025