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: September 3, 2018  December 1, 2018
Reading Week: October 15, 2018  October 20, 2018
Date 
Start Time 
End Time 
Venue 
Remark 
Monday 
1:30pm 
2:50pm 
MB256 (Main Bldg) 

Wednesday 
1:30pm 
2:50pm 
CPD3.29 (Podium Level 3, Centennial Campus) 

