Convex Optimization I
Fee may apply
Gain the necessary tools and training to recognize convex optimization problems that confront the engineering field. Learn the basic theory of problems including course convex sets, functions, and optimization problems with a concentration on results that are useful in computation. Develop a thorough understanding of how these problems are solved and the background required to use the methods in research or engineering work.
Solid knowledge of linear algebra as in EE263 and basic probability. Exposure to numerical computing, optimization, and application fields helpful but not required; the engineering applications will be kept basic and simple.
- Optimality conditions, duality theory, theorems of alternative and applications
- Least-squares, linear and quadratic programs, semidefinite programming and geometric programming
- Numerical algorithms for smooth and equality constrained problems
- Interior-point methods for inequality constrained problems
- Applications to signal processing, communications, control, analog and digital circuit design, computational geometry, statistics, machine learning and mechanical engineering
Note on Course Availability
The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate certificate homepage.
|Dates:||January 8 - March 14, 2019|
|Times:||9:00 am - 10:20 am|
Provides Stanford University credit that may later be applied towards a graduate degree or certificate. Includes access to online course materials and videos for the duration of the academic quarter. Starting Autumn 2016 there is a $100 fee per course for courses dropped before the drop deadline. Click here for more information about our Registration Policies.
NotesEnrollment Dates: October 28 to December 10, 2018
This course may not currently be available to residents of certain states.