Home Work
| Instructor: | Jim Burke | E-Mail: | burke(at)math(dot)washington(dot)edu | |
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| Phone: | 543-6183 | Hours: | MWF 11:30-12:10pm | |
| Office: | C-443 Padelford | & by appointment | ||
| Prerequisites: | Mulivariable Calculus, Linear Algebra, MatLab | Classroom: | Padelford C401 | |
The text for this course is CONVEX OPTIMIZATION based on a very successful graduate EE course in convex optimization designed by Stephen Boyd of Stanford University. The study of convex optimization and convex analysis in general dates back to antiquity. However, a systematic study began at the beginning of the 20th century. The development of the subject accelerated significantly in the last half century, and there has been an explosion of new develpments and applications within the last 20 years. This rapid growth continues today with no end in sight. Just within the past 10 years at least 20 news texts have been written on the topic, all making different contributions and providing different perspectives on the topic. Many factors have contributed the enormous growth of this topic. The most important of these are (1) the mathmatical foundations of the subject are now well developed, (2) new numerical methods exist for the accurate and rapid solution of a wide range of convex optimization problems, and (3) new modeling paradigms have been developed permitting the modeling or approximation of an enourmous range of applied problems in engineering, finance, computational geometry, and management science. In this course, I intend to focus on applications with the aim of developing developing the skills necessary to recognize and model convex optimization problems that arise in applications. In order to do this, we need to develop some understanding of the mathematical tools and structures from convex analysis. Finally, I hope to introduce some of the modern numerical methods that can be applied to solve convex optimization problems.
Course notes by Laura Elisa Celis.
| Mathematics Department |
| University of Washington |