General Course Information
- Introduction to optimization theory and methods,
with applications in systems, control, and communication.
- Analysis of optimization problems.
- Optimization algorithms.
- Ability to make precise statements about optimization problems.
Brief Course Description
- Unconstrained and constrained optimization theory
- Algorithms and search methods for
optimization, and their analysis (includes: quasi-Newton, recursive least
squares, genetic algorithm)
- Optimization of dynamic systems
- Examples from various engineering applications
- Working knowledge of linear algebra (matrix manipulations,
vector spaces, bases, eigenvalues, quadratic forms)
- Working knowledge of calculus of several variables (differentiating
functions of n variables, chain rule, gradients, Taylor series,
- Basic state space systems in discrete time (desirable but not
- An appreciation of rigor.
See Organizational Information.
Examples of applications
- Optimal control
- Parameter estimation
- Optimal design
- Neural network training
- Optimal pricing
- Investment planning
- Machine intelligence
Professor Edwin K. P. Chong
Course web pages:
- E-mail: (preferred mode)
- Phone: 970-491-7858
- Fax: 970-491-2249
Professor Edwin K. P. Chong,
This document was last modified
January 03, 2020.