I taught this course in Spring 2008 at McMaster University.
Course contents
- How to model different engineering problems?
- How to choose between the various models?
- How to exploit the structure of the problem?
- What solver to use for a given model?
- Various applications and modelling tools
Assignments
Final project
Each student will be required to prepare a project, which investigates a certain optimization problem, possible approaches to solve it, different models, etc. You can come up with a problem of your own or can consult me for some ideas. It can also be something you worked on before. A brief one page sketch of what you want to do is due by February 29. The final project paper (around 10 pages) is due by April 22.
Marking scheme
Assignments:40% altogether
Project: 60%
No midterm.
Course material
Lectures 1-5 follow the lecture slides of Boyd and Vandenberghe. Lecture 6 is from me, about optimization software. Lecture Lecture 7 is the 6th Lecture of Boyd and Vandenberghe. Lecture 8 include slides from Ben-Tal and Nemirovski and a few from Boyd and Vandenberghe. Lecture 9 is based on several sources.
Lecture 4: Convex optimization problems
Lecture 6: Forming and Solving Optimization Problems
Lecture 7: Approximation and fitting
Lecture 8: Optimization under uncertainty: robust optimization
Lecture 9: Modelling with semidefinite and second order cones
Lecture 10: Software demonstrations
Further readings
S. Boyd and L. Vandenberghe: Convex Optimization. Cambridge University Press, 2004.
A. Ben-Tal and A. Nemirovskii: Lectures on Modern Convex Optimization, Analysis, Algorithms, And Engineering Applications. MPS-SIAM Series on Optimization, SIAM Publications, 2001.
Transparencies for this book are here in PDF.
SeDuMi: Software for linear/second order/semidefinite optimization.
CVX: Matlab Software for Disciplined Convex Programming
