Lars Ruthotto

MATH 347 - Introduction to Nonlinear Optimization

Instructor:
Prof. Lars Ruthotto
Description:
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.
Prerequisites:
Math 211, Math 221 or 321, Math 250, and CS 170
Times:
Lectures: TueTh 2:30 PM - 3:45 PM, Math & Science Center, Room W303
First day of classes: August 25, 2016
Last day of classes: December 6, 2016
Tests: tba
Final exam: tba
Recess: October 11 (Fall break) and November 24 (Thanksgiving)
Literature:
Required reading is:
Introduction to Nonlinear Optimization by A. Beck
Additional texts for reference are:
Numerical Optimization by J. Nocedal and S.J. Wright
Convex Optimization by S. Boyd and L. Vandenberghe
Syllabus:
The syllabus can be found here.
Material:
Additional course material will be made available using Canvas.
    Unconstrained optimization:
Math347-Himmelblau
    Convex optimization:
Math347-Himmelblau
    Constrained optimization:
Math347-Himmelblau