AMS229: Convex Optimization

This course focuses on recognizing, formulating, analyzing and solving convex optimization problems encountered across science and engineering. Topics include convex sets; convex functions; convex optimization problems; duality; subgradient calculus; algorithms for smooth and non-smooth convex optimization; applications to signal and image processing, machine learning, statistics, control, robotics and economics. Students are required to have knowledge of calculus and linear algebra, and exposure to probability. Enrollment restricted to graduate students.

5 credits

Year	Fall	Winter	Spring	Summer

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