MATH4230  Optimization Theory  2023/24
Announcement
 The midterm will be on March 12, 2024, Tuesday.
 The miniproject is released on Blackboard. You may form a group of at most 2 students and hand in a report of length between 5 and 10 pages before 2024/4/13, 11:59 pm.
General Information
Lecturer

Zeng Tieyong
 Office: LSB 225
 Tel: (852) 39437966
 Email:
Teaching Assistant

Zeyu Li
 Office: LSB 222A
 Tel: 3943 3575
 Email:

Shengze Xu
 Office: LSB 222C
 Tel: 3943 8570
 Email:
Time and Venue
 Lecture: Tu 14:30  16:15 ( Mong Man Wai Bldg 703); We 14:30  15:15 (Science Centre L3)
 Tutorial: We 15:30  16:15 (Science Centre L3)
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
 G. Lan, Firstorder and Stochastic Optimization Methods for Machine Learning, Spriner 2020.
 D. Michael Patriksson, An Introduction to Continuous Optimization: Foundations and Fundamental Algorithms, Third Edition (Dover Books on Mathematics), 2020
 D. Bertsekas, Convex Optimization Theory, Athena Scientific, 2009.
 Boris S. Mordukhovich, Nguyen Mau Nam An Easy Path to Convex Analysis and Applications, 2013.
References
 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 Algorithms, Athena Scientific, 2015.
Lecture Notes
Class Notes
 Introduction to optimizationJan9, 2024
 Mathematical reviewJan 16, 2024
 Convex functionFeb 6, 2024
 SubgradientFeb 14, 2024
 MoreauRockafellar ThmFeb 28, 2024
 Support Vector MachinesMarch 26, 2024
 KKT conditionApril 2, 2024
 Normal cone
 Conjugate functionsApril 10, 2024
 Gradient descent April 16, 2024
Tutorial Notes
 Convex Set
 Relative Interior
 Convex Function
 Conjugate Function
 Subgradient
 Lipschitz Continuous
 KKT Condition
 Fenchel Conjugate
Assignments
 Assignment 1
 Assignment 2
 Assignment 3
 Assignment 4
 Assignment 5
 Assignment 6
 Assignment 7
 Assignment 8
 Assignment 9
 Assignment 10
Solutions
Assessment Scheme
Tutorial attendance & good efforts or top 15% in both the mid and final exams (tutorial assignments are counted only if they are submitted before 6:30pm Monday next after the tutorial class)  10%  
MidExam  17.5%  
MiniProject  17.5%  
FinalExam  55% 
Useful Links
 Convex Optimization 2008 of illinois
 Convex Optimization (Book Stanford)
 Convex Optimization(Georgia Tech 2022)
 CONVEX ANALYSIS: An introduction to convexity and nonsmooth analysis
 An Easy Path to Convex Analysis and Applications
 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: April 16, 2024 12:36:10