MATH 347 - Introduction to Nonlinear Optimization


I established this course in the fall semester of 2016 and have also taught the class in the fall of 2018 and spring 2020. Given the high demand, I hope the class can be offered once per academic year.


Nonlinear optimization problems arise in a wide range of applications, for example, in economics, physics, engineering, machine learning, and imaging. This introductory course covers a variety of relevant unconstrained and constrained optimization problems. While its emphasis is on theory, we will also discuss real-world applications and solve small-scale, smooth optimization problems numerically.


Math 211, Math 221 or 321, Math 250, and CS 170


The primary textbook for the course is the following: Introduction to Nonlinear Optimization by A. Beck. Reading the textbook is not required, but it is recommended. I will provide lecture notes with detailed references for each lecture. You are not responsible for textbook material that is not covered in lecture.

Lars Ruthotto
Lars Ruthotto
Winship Distinguished Research Associate Professor of Mathematics and Computer Science