Optimization Theory (2022 Fall)
|Hours||3 hours (M 17:00-19:45)|
|Instructor||Prof. Hyunggon Park|
|Office||Engineering A Bldg. 514|
This course covers linear programming and convex optimization techniques which can be core mathematical tools for engineering problems.
- Linear algebra
Lectures with homework assignments
Concentrates on recognizing and solving linear and convex optimization problems that arise in engineering. Linear programming, duality, simplex method, convex sets, functions, and optimization problems. Optimality conditions, duality theory, theorems of alternative, and applications. Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering.
- Homework (20%)
- Midterm Exam (30%)
- Final Exam (50%)
- S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press. (Not mandatory)
- M. J. Kochenderfer and T. A. Wheeler, "Algorithms for Optimization, MIT Press
- D. Bertsimas and J. N. Tsitsiklis, Introduction to linear optimization, Athena Scientific.
- D. Bertsekas, Nonlinear Programming, Athena Scientific.
Tentative Course Outline
A tentative list of the covered topics:
- Review of basic linear algebra
- Geometry of linear programming problems
- Linear programming problems
- The simplex method
- Convex set, convex functions
- Convex optimization problems
- Numerical linear algebra
- Other related topics