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; integer programming; duality and Lagrangian relaxation; Newton’s method; Simplex algorithm; interior point method; a brief introduction to game theory. 
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 16, 2017  April 29, 2017
Reading Week: March 6, 2017  March 11, 2017
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 

