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.
You should have good knowledge of linear algebra and exposure to probability. Exposure to numerical computing, optimization, and application fields is helpful but not required; the applications will be kept basic and simple. You will use matlab and CVX to write simple scripts, so some basic familiarity with matlab is helpful. We will provide some basic Matlab tutorials.
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Kai1986completed this course, spending 10 hours a week on it and found the course difficulty to be medium.
the cvx101 is a very good course that displays the topic of the mathematical convex programming from a very practital point of view with a lot of very interesting applications and showing how to solve them. I suggest this course mainly to people that have at least a Bachelor degree in engineering field
This is an amazing course. Teaches the theory behind and to solve numerically convex optimization problems. Hws are solved writing progams in Matlab making use of the cvx library (developed by Prof. Boyd among others) which make programming convex optimization problem very natural and easy