Convex Optimization and Applications (MA030136)
This course introduces the basic theory in convex optimization and illustrates its use in recent successful applications such as sparse learning, blind source separation, low-rank optimization, image processing, regression and classification, phase retrieval.
Through the course, students will study convex sets and functions and their properties, duality and dual maximization problem with the same optimal value, a certificate of optimality for an optimization problem. Students will also learn some commonly known convex optimization forms such as Linear Program, Quadratic Program, Second Order Cone Programs, Semi-Definite Programs etc. Students will know practical tools, and able to recognize and formulate convex optimization problems and solve them using efficient solvers.