Syllabus 
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; vector optimization; integer programming; duality and Lagrangian relaxation; Newton’s method; interior point method; ellipsoid method; subgradient algorithm and decomposition method. 
Topics 
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. 
Timetable 
Teaching Period: January 19, 2015  May 2, 2015
Reading Week: March 9, 2015  March 14, 2015
Date 
Start Time 
End Time 
Venue 
Remark 
Tuesday 
10:30am 
12:00nn 
Room 308, Chow Yei Ching Bldg 

Thursday 
10:30am 
12:00nn 
Room 308, Chow Yei Ching Bldg 

