Network Optimisation (MAST90013)

Graduate courseworkPoints: 12.5Not available in 2020

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As part of the University’s response to COVID-19 and the associated Government restrictions and guidelines, most subjects will continue to be delivered online in Winter and Semester 2.
For information about the University’s phased return to campus and in-person activity in Winter and Semester 2, please refer to the on-campus subjects page.

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Overview

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Many practical problems in management, operations research, telecommunication and computer networking can be modelled as optimisation problems on networks. Here the underlying structure is a graph. This subject is an introduction to optimisation problems on networks with a focus on theoretical results and efficient algorithms. It covers classical problems that can be solved in polynomial time, such as shortest paths, maximum matchings, maximum flows, and minimum cost flows. Other topics include complexity and NP-completeness, matroids and greedy algorithms, approximation algorithms, multicommodity flows, and network design. This course is beneficial for all students of discrete mathematics, operations research, and computer science.

Intended learning outcomes

After completing this subject, students should:



  • be able to understand aspects of network optimisation problems and the methodologies to solve them;
  • develop the abilities needed to design combinatorial algorithms for solving other network problems not covered in the subject;
  • have the ability to pursue further studies in this and related areas.

Generic skills

In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. These include:

  • problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
  • analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
  • collaborative skills: the ability to work in a team;
  • time-management skills: the ability to meet regular deadlines while balancing competing commitments.

Last updated: 3 November 2022

Eligibility and requirements

Prerequisites

The following subject, or equivalent:

Code Name Teaching period Credit Points
MAST30011 Graph Theory
Semester 1 (On Campus - Parkville)
 12.5

Corequisites

None

Non-allowed subjects

None

Recommended background knowledge

An introductory-level subject in operations research equivalent to

Code Name Teaching period Credit Points
MAST20018 Discrete Maths and Operations Research
Semester 2 (On Campus - Parkville)
 12.5

Inherent requirements (core participation requirements)

The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.

Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home

Last updated: 3 November 2022

Assessment

Due to the impact of COVID-19, assessment may differ from that published in the Handbook. Students are reminded to check the subject assessment requirements published in the subject outline on the LMS

DescriptionTimingPercentage

Up to 50 pages of written assignments (two assignments worth 15% each, due mid and late in semester)

  • 50 pages
Second half of the teaching period30%

A written examination

  • 3 hours
During the examination period70%

Last updated: 3 November 2022

Dates & times

Not available in 2020

Time commitment details

170 hours

Last updated: 3 November 2022

Further information

  • Texts

    Prescribed texts

    Lecture notes prepared by Dr Sanming Zhou, and the textbook by B. Korte and J. Vygen, Combinatorial Optimiation: Theory and Algorithms. 2nd Edition, Springer, Berlin, 2002

    Recommended texts and other resources

    TBA

  • Related Handbook entries

    This subject contributes to the following:

    TypeName
    CourseMaster of Science (Mathematics and Statistics)
    CoursePh.D.- Engineering
    CourseDoctor of Philosophy - Engineering
    CourseMaster of Philosophy - Engineering
    Informal specialisationMathematics and Statistics
  • Available through the Community Access Program

    About the Community Access Program (CAP)

    This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.

    Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants

    Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.

  • Available to Study Abroad and/or Study Exchange Students

    This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.

Last updated: 3 November 2022