MATH4230  Optimization Theory  2019/20
Announcement
 Course Outline [Download file]
 There will be no tutorial class in the first week.
 Project Specification [Download file]
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, 2013
 D. Bertsekas, Convex Optimization Algorithms, Athena Scientific, 2015.
Preclass Notes
 Convex optimization prequisites review, by Nicole Rafidi
 Nonvertical separation
 Basic properties of limsup and liminf
Lecture Notes
 Lecture notes of CUHK
 Convex Optimization: Fall 2019 (CMU,with permission)
 Notes of MIT (with permission)
 Notes of Nemirovski (with permission)
 Notes of Stanford
 Convex Optimization (UIUC)
 Convex Optimization, Spring 2017, Notes (Gatech)
 ProximalADMM(wen zaiwen)
 Notes for Newton’s Method for Unconstrained Optimization (MIT)
 Notes for subdifferential calculus
 ADMM
 ADMM
Class Notes
 Convex sets, and Convex FunctionsJan82020
 Convexity and Continuity (Jan152020)
 Convex functions (Feb182020)
 Gradient method (Feb192020)
 Fenchel Conjugate (Feb 252020)
 subgradientzeng (Feb 262020)
 Subgradients
 Gradient descent
 Subgradients
 subgradients
 KarushKuhnTucker conditions
 Duality and KTT
 Weak Duality
 Strong Duality
 Newton’s method
 Newton’s method
 Proximal Algorithms
 Proximal Algorithms
 Proximal Gradient Algorithms
 Perspective function(Jan82020)
 Caratheodory’s Theorem (Jan142020)
 Existence of Solutions and Optimality Conditions
 Conjugate functions
 Primal and dual problems
Tutorial Notes
Assignments
Solutions
Useful Links
 Convex Optimization 2008 of illinois
 Convex Optimization (Book Stanford)
 Convex Optimization(Georgia Tech 2017)
 Convex Optimization(CMU Fall 2019)
 An Easy Path to Convex Analysis and Applications
 Convex Optimization in Normed Spaces (2014)
 Convex analysis (Ecole Polytechnique)
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: February 26, 2020 14:07:49