Enquiry for Science Major/Minor/Programme Requirements
MATH3901 Operations research I (6 credits) Academic Year 2026
Offering Department Mathematics Quota ---
Course Co-ordinator Prof L Lai, Mathematics < lai.lexiao@hku.hk >
Teachers Involved (Prof L Lai,Mathematics)
Course Objectives The objective is to provide a fundamental account of the basic results and techniques of Linear Programming (LP) and its related topics in operations research. The topics include the simplex method, the dual simplex method, parametric programming, decomposition methods and interior point methods.
Course Contents & Topics - Linear programming
- Duality theory
- Sensitivity analysis and parametric linear programming
- Ellipsoid methods
- Interior point methods
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 understand the fundamental concept and approach of linear programming appropriate to the further study of operations research
CLO 2 demonstrate knowledge and understanding of the underlying techniques of the simplex method and its extensions such as the dual simplex algorithm and the decomposition method
CLO 3 understand and apply the theory of integer programming
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in MATH2014 or MATH2101 or MATH2822.
Course Status with Related Major/Minor /Professional Core 2026 Major in Mathematics ( Disciplinary Elective )
2026 Major in Mathematics (Intensive) ( Disciplinary Elective )
2026 Minor in Mathematics ( Disciplinary Elective )
2026 Minor in Operations Research & Mathematical Programming ( Core/Compulsory )
2025 Major in Mathematics ( Disciplinary Elective )
2025 Major in Mathematics (Intensive) ( 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 ( Disciplinary Elective )
2024 Major in Mathematics ( Disciplinary Elective )
2024 Major in Mathematics (Intensive) ( 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 ( Disciplinary Elective )
2023 Major in Mathematics ( Disciplinary Elective )
2023 Major in Mathematics (Intensive) ( 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 ( Disciplinary Elective )
2022 Major in Mathematics ( Disciplinary Elective )
2022 Major in Mathematics (Intensive) ( Disciplinary Elective )
2022 Minor in Mathematics ( Disciplinary Elective )
2022 Minor in Operations Research & Mathematical Programming ( Core/Compulsory )
Course to PLO Mapping 2026 Major in Mathematics < PLO 1,2,3 >
2026 Major in Mathematics (Intensive) < PLO 1,2,3 >
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 1,2,3,5 >
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 1,2,3,5 >
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 1,2,3,5 >
2022 Major in Mathematics < PLO 1,2,3 >
2022 Major in Mathematics (Intensive) < PLO 1,2,3 >
Offer in 2026 - 2027 Y        2nd sem    Examination May     
Offer in 2027 - 2028 Y
Course Grade A+ to F
Grade Descriptors
A Demonstrate an excellent understanding of key concepts and ideas by being able to identify basic principles, appropriate theorems, algorithms 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 to solve problems with some innovative approaches.
B Demonstrate a good understanding of key concepts and ideas by being able to identify basic principles, appropriate theorems, algorithms 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 identify basic principles, appropriate theorems, algorithms and their applications 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 identify basic principles, appropriate theorems, algorithms and their applications 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 basic principles, appropriate theorems, algorithms or their applications, or not being able to complete or compute 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
Assignments Coursework assessment 10.0 1,2,3
Examination 50.0 1,2,3
Test Two midterm tests 40.0 1,2,3
Required/recommended reading
and online materials		 J.P. Ignizio and T.M. Cavalier: Linear Programming (Prentice-Hall International, 1994)
D. Bertsimas and J.N. Tsitsiklis: Introduction to Linear Optimization (Athena Scientific, 1997)
W.L. Winston: Introduction to Mathematical Programming (Duxbury 4/e 2003)
Course Website http://moodle.hku.hk/
Additional Course Information


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