MATH4230  Optimization Theory  2018/19
Announcement
 Course Outline [Download file]
 There will be no tutorial class in the first week.
 Midterm will be held at LSB LT4,12PM on Mar 6
 Project Specification [Download file]
 Final Exam : 7 May (Tue), 15:3017:30, Sir Run Run Shaw Hall(Auditorium)
General Information
Lecturer

Prof. Zeng Tieyong
 Email:
Teaching Assistant

Wong Hok Shing
 Email:
Time and Venue
 Lecture: Tue 2:30pm  4:15pm, LSB LT4; 1:30pm  2:15pm, LSB LT4
 Tutorial: Wed 12:30pm  1:15pm, LSB LT4
Course Description
Unconstrained and equality optimization models, constrained problems, optimality conditions for constrained extrema, convex sets and functions, duality in nonlinear convex programming, descent methods, conjugate direction methods and quasiNewton methods. Students taking this course are expected to have knowledge in advanced calculus.
Textbooks
 S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004.
 D. Bertsekas, A. Nedic, A. Ozdaglar, Convex Analysis and Optimization, Athena Scientific, 2003.
 D. Bertsekas, Convex Optimization Theory, Athena Scientific, 2009.
 Boris S. Mordukhovich, Nguyen Mau Nam An Easy Path to Convex Analysis and Applications, 2014
Preclass Notes
Lecture Notes
 Notes of Stanford
 Notes of Nemirovski (with permission)
 Notes of MIT (with permission)
 Notes for Newton’s Method for Unconstrained Optimization (MIT)
 Notes for subdifferential calculus
 ADMM
 ADMM
 ProximalADMM(wen zaiwen)
Class Notes
 Convex sets, and Convex Functions
 Convexity and Continuity
 Conjugate functions
 Existence of Solutions and Optimality Conditions
 Gradient descent
 Primal and dual problems
 Subgradients
 Gradient method
 Subgradients
 subgradients
 KarushKuhnTucker conditions
 Duality and KTT
 Weak Duality
 Strong Duality
 Newton’s method
 Newton’s method
 Proximal Algorithms
 Proximal Algorithms
 Proximal Gradient Algorithms
Tutorial Notes
 Proof of Caratheodory's Theorem
 Convex Function
 Conjugate Function
 Relative interior
 Optimal conditions
 MoreauRockafellar Theorem
 Normal Cone
 Gradient descent
 ADMM
Assignments
 Exercise 1
 Exercise 2
 Exercise 3
 Exercise 4
 Exercise 5 (3b modified)
 Exercise 6
 Exercise 7
 Exercise 8
 Exercise 9
 Exercise 10
Solutions
 Exercise 1 Solution
 Exercise 2 Solution
 Exercise 3 Solution
 Exercise 4 Solution
 Exercise 5 Solution
 Exercise 6 Solution
 Midterm Solution
 Exercise 7 Solution
 Exercise 8 Solution
 Exercise 9 Solution
 Exercise 10 Solution
Useful Links
 Convex Optimization 2008 of illinois
 Convex Optimization (Book Stanford)
 Convex Optimization(Georgia Tech 2017)
 Convex Optimization(CMU Fall 2015)
 An Easy Path to Convex Analysis and Applications
 Convex Optimization in Normed Spaces (2014)
Honesty in Academic Work
The Chinese University of Hong Kong places very high importance on honesty in academic work submitted by students, and adopts a policy of zero tolerance on cheating and plagiarism. Any related offence will lead to disciplinary action including termination of studies at the University. Although cases of cheating or plagiarism are rare at the University, everyone should make himself / herself familiar with the content of the following website:
http://www.cuhk.edu.hk/policy/academichonesty/and thereby help avoid any practice that would not be acceptable.
Assessment Policy Last updated: April 23, 2019 17:43:33