|COMP9602 - Convex Optimization|
|Semester 1, 2018-19|
|This is a Graduate Course. MPhil/PhD students in the Department of Computer Science should read the Coursework Requirement.|
|Instructor||Dr. C. Wu|
This course presents the theory, algorithms and applications of convex optimization. Main topics to be included: convex sets and functions; linear programming; quadratic programming, semidefinite programming, geometric programming; integer programming; duality and Lagrangian relaxation; Newton’s method; Simplex algorithm; interior point method; a brief introduction to game theory.
On the theory of convex optimization: convex set and functions, linear programming, quadratic programming, semidefinite programming, geometric programming, integer programming, vector optimization, duality theory (dual, Lagrange multiplier, KKT conditions), etc.
On algorithms to solve convex optimization problems: gradient descent algorithm, Newton’s method, interior point method, ellipsoid method, subgradient algorithm, etc.
Teaching Period: September 3, 2018 - December 1, 2018