MATH4230 - Optimization Theory - 2022/23

Course Name: 
Course Year: 

General Information


  • Zeng Tieyong
    • Office: LSB 225
    • Tel: (852) 3943-7966
    • Email:

Teaching Assistant

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

Time and Venue

  • Lecture: Tu 14:30 - 16:15 ( Mong Man Wai Bldg 702); We 13:30 - 14:15 (William M W Mong Eng Bldg 404)
  • Tutorial: We 12:30 - 13:15 (William M W Mong Eng Bldg 404)

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 quasi-Newton methods. Students taking this course are expected to have knowledge in advanced calculus.


  • G. Lan, First-order 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.


  • 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

Tutorial Notes


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%
Mid-Exam 17.5%
Mini-Project 17.5%
Final-Exam 55%

Useful Links

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Assessment Policy

Last updated: January 19, 2023 10:44:17