COMP9602 - Convex Optimization
Semester 2, 2014-15
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; vector optimization; integer programming; duality and Lagrangian relaxation; Newton’s method; interior point method; ellipsoid method; subgradient algorithm and decomposition method.


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.

Pre-requisites Linear algebra
Instructor's web  
  • In-course assessment:
  • Examination marks:

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  
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