Description

Many problems in mathematics, computational science, statistics and engineering, may be posed as an optimization problem. These problems are categorized based on the linearity or nonlinearity of the objective function and constraints and the nature of the decision variables. The numerical algorithms designed to solve these problems may be deterministic or stochastic, gradient-based or derivative–free. This course is intended for graduate students in mathematics, computer science, and other applied science and engineering disciplines where numerical optimization problems arise.

Objectives

This course will provide students with an overview of numerical approaches for linear and nonlinear optimization problems with a focus on theory, implementation and computation.

Prerequisites

Undergraduate linear algebra at the level of Math-2051, multivariable Calculus, experience with programming (Matlab, Python or R preferred), experience with computer simulation.

Credit restrictions

Math-6202 is credit restricted with Comp-6933.

Texts

• Beck, Introduction to Nonlinear Optimization, SIAM, 2014.
• Griva, Nash & Sofer, Linear and Nonlinear Optimization, SIAM, 2009.
• Ferris, Mangasarian, Wright, Linear Programming with MATLAB, SIAM, 2007.