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
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.

Pre-requisites Linear algebra
Compatibility  
Instructor's web  
Assessment
  • In-course assessment:
  • Examination marks:
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 CPD-3.29 (Podium Level 3, Centennial Campus)

 

Discussion board  

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