- 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.

- 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

- E. K. P. Chong and S. H. Żak,
*An Introduction to Optimization, Fourth Edition*, New York, NY: John Wiley & Sons, Inc. (Wiley-Interscience Series), 2013.

- 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, limits)
- Basic state space systems in discrete time (desirable but not required).
- An appreciation of rigor.

- Optimal control
- Parameter estimation
- Optimal design
- Neural network training
- Optimal pricing
- Investment planning
- Machine intelligence

- E-mail: (preferred mode)
- Phone: 970-491-7858
- Fax: 970-491-2249

- Index: http://www.engr.colostate.edu/~echong/ece520/
- Password protected page (Username and password will be provided in class)

Professor Edwin K. P. Chong, This document was last modified January 03, 2020.