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MATH3171 is a Mathematics Level III course.

Units of credit: 6

Prerequisites: (1) both [MATH2011 or MATH2111] and [MATH2501 or MATH2601]; or (2) both MATH2069 (CR) and MATH2099 ; or (3) both [MATH2018 or MATH2019] (DN) and MATH2089

Exclusion: MATH5171 Linear and Discrete Optimization Modelling (jointly taught with MATH3171) 

Cycle of offering:  Term 3 2023 and 2025 (odd years) 

Graduate attributes: The course will enhance your research, inquiry and analytical thinking abilities.

More information: Please refer to the Course Outline.

Important additional information as of 2023

UNSW Plagiarism Policy

The University requires all students to be aware of its .

For courses convened by the School of Mathematics and Statistics no assistance using generative AI software is allowed unless specifically referred to in the individual assessment tasks.

If its use is detected in the no assistance case, it will be regarded as serious academic misconduct and subject to the standard penalties, which may include 00FL, suspension and exclusion.

Please note the following recent changes to the programs 3956 and 3962, in Applied Mathematics.

1.       From 2022 there will be 3 new courses:

• MATH3051 to be offered in T3 every year. All students who will be doing level 3 in Applied Maths in 2022 and 2023 will be strongly advised to take this course as an elective course. From 2024 this course will be one of two core courses.
• MATH3371/5371 to be offered in T1 every year
• MATH3191/5191 to be offered in T3, alternate with MATH3171/5171

2.       From 2024 all level 3 students in Applied Maths should note that MATH3051 and MATH3041 will be one of two possible core courses.

If you are currently enrolled in MATH3171, you can log into  for this course.

Optimization is the mathematical problem of finding a decision to achieve the best possible outcome while satisfying the restriction we faced. Linear programs, conic linear programs and discrete optimization problems arise in a myriad of applications: electricity markets, airlines, logistics, public transport, international shipping, mining, finance, engineering, and data science. This course will provide an introduction to the basic mathematical theory, modelling techniques, computational methods and selected applications of linear, conic and discrete optimization.