Convex Optimization
This course concentrates on recognizing and solving convex optimization problems that arise in applications. The syllabus includes: convex sets, functions, and optimization problems; basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory, theorems of alternative, and applications; interior-point methods; applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance.

Meet your instructors
Frequently asked questions
Do I need to buy the textbook?
No, the textbook is available online at http://www.stanford.edu/~boyd/cvxbook/.
Do we need to purchase a Matlab license to take this course?
A Matlab licence or access is NOT included in this course. Trial versions of Matlab may be available at https://www.mathworks.com/
How hard is this class?
This is an advanced class, targeting MS and PhD level students in mathematically sophisticated fields.
Who can take this course?
Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.