Enquiry for Science Major/Minor/Programme Requirements
MATH3904 Introduction to optimization (6 credits) Academic Year 2025
Offering Department Mathematics Quota ---
Course Co-ordinator Prof W Zang, Mathematics < wzang@maths.hku.hk >
Teachers Involved (Prof W Zang,Mathematics)
Course Objectives This course introduces students to the theory and techniques of optimization, aiming at preparing them for further studies in operations research, mathematical economics and related subject areas.
Course Contents & Topics - Unconstrained and constrained optimization.
- Necessary conditions and sufficient conditions for optimality, convexity, duality.
- Algorithms and numerical examples.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 demonstrate knowledge and understanding of the basic theory and techniques of optimization
CLO 2 solve various optimization problems encountered in practice
CLO 3 understand the connection between the purely analytical character of an optimization problem and the behavior of algorithms for solving it
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in (MATH2101 and MATH2211) or MATH2014 or (MATH1821 and MATH2822)
Course Status with Related Major/Minor /Professional Core 2025 Major in Mathematics ( Disciplinary Elective )
2025 Major in Mathematics (Intensive) ( Core/Compulsory )
2025 Minor in Computational & Financial Mathematics ( Disciplinary Elective )
2025 Minor in Mathematics ( Disciplinary Elective )
2025 Minor in Operations Research & Mathematical Programming ( Core/Compulsory )
2024 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Core/Compulsory )
2024 Major in Decision Analytics ( Core/Compulsory )
2024 Major in Mathematics ( Disciplinary Elective )
2024 Major in Mathematics (Intensive) ( Core/Compulsory )
2024 Minor in Computational & Financial Mathematics ( Disciplinary Elective )
2024 Minor in Mathematics ( Disciplinary Elective )
2024 Minor in Operations Research & Mathematical Programming ( Core/Compulsory )
2023 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Core/Compulsory )
2023 Major in Decision Analytics ( Core/Compulsory )
2023 Major in Mathematics ( Disciplinary Elective )
2023 Major in Mathematics (Intensive) ( Core/Compulsory )
2023 Minor in Computational & Financial Mathematics ( Disciplinary Elective )
2023 Minor in Mathematics ( Disciplinary Elective )
2023 Minor in Operations Research & Mathematical Programming ( Core/Compulsory )
2022 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Core/Compulsory )
2022 Major in Decision Analytics ( Core/Compulsory )
2022 Major in Mathematics ( Disciplinary Elective )
2022 Major in Mathematics (Intensive) ( Core/Compulsory )
2022 Minor in Computational & Financial Mathematics ( Disciplinary Elective )
2022 Minor in Mathematics ( Disciplinary Elective )
2022 Minor in Operations Research & Mathematical Programming ( Core/Compulsory )
2021 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Core/Compulsory )
2021 Major in Decision Analytics ( Core/Compulsory )
2021 Major in Mathematics ( Disciplinary Elective )
2021 Major in Mathematics (Intensive) ( Core/Compulsory )
2021 Minor in Computational & Financial Mathematics ( Disciplinary Elective )
2021 Minor in Mathematics ( Disciplinary Elective )
2021 Minor in Operations Research & Mathematical Programming ( Core/Compulsory )
Course to PLO Mapping 2025 Major in Mathematics < PLO 1,2,3 >
2025 Major in Mathematics (Intensive) < PLO 1,2,3 >
2024 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 1,2,3,4,5 >
2024 Major in Decision Analytics < PLO 1,3,4 >
2024 Major in Mathematics < PLO 1,2,3 >
2024 Major in Mathematics (Intensive) < PLO 1,2,3 >
2023 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 1,2,3,4,5 >
2023 Major in Decision Analytics < PLO 1,3,4 >
2023 Major in Mathematics < PLO 1,2,3 >
2023 Major in Mathematics (Intensive) < PLO 1,2,3 >
2022 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 1,2,3,4,5 >
2022 Major in Decision Analytics < PLO 1,3,4 >
2022 Major in Mathematics < PLO 1,2,3 >
2022 Major in Mathematics (Intensive) < PLO 1,2,3 >
2021 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 1,2,3,4,5 >
2021 Major in Decision Analytics < PLO 1,3,4 >
2021 Major in Mathematics < PLO 1,2,3 >
2021 Major in Mathematics (Intensive) < PLO 1,2,3 >
Offer in 2025 - 2026 Y        1st sem    Examination Dec     
Offer in 2026 - 2027 Y
Course Grade A+ to F
Grade Descriptors
A Demonstrate an excellent understanding of key concepts and ideas by being able to identify the appropriate theorems and their applications through correctly analysing problems, clearly and elegantly presenting correct logical reasoning and argumentation and being able to carry out computations carefully and correctly, and with some innovative approaches to solving problems.
B Demonstrate a good understanding of key concepts and ideas by being able to identify the appropriate theorems and their applications through correctly analysing problems, but with some minor inadequacies in arguments, identifying the appropriate theorems or their applications and presentation or with some minor computational errors.
C Demonstrate an acceptable understanding of key concepts and ideas by being able to correctly identify appropriate theorems, but with some inadequacies in applying the theorems through incorrectly analysing problems with poor argument and presentation or a number of minor computational errors.
D Demonstrate some understanding of key concepts and ideas by being able to correctly identify appropriate theorems, but with substantial inadequacies in applying the theorems through incorrectly analysing problems with poor argument or presentation or with substantial computational errors.
Fail Demonstrate poor and inadequate understanding by not being able to identify appropriate theorems or their applications, or not being able to complete the solution.
Communication-intensive Course N
Course Type Lecture-based course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Lectures 36.0
Tutorials 12.0
Reading / Self study 100.0
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Examination 50.0 1,2,3
Test 50.0 1,2,3
Required/recommended reading
and online materials		 Instructor's lecture notes
Course Website http://moodle.hku.hk/
Additional Course Information


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