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Approximation, Algorithms and Heuristics (MAST90098)
Graduate courseworkPoints: 12.5Dual-Delivery (Parkville)
Please refer to the return to campus page for more information on these delivery modes and students who can enrol in each mode based on their location.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 2
Overview
Availability | Semester 2 - Dual-Delivery |
---|---|
Fees | Look up fees |
Many discrete optimisation problems encountered in practice are too difficult to solve exactly in a reasonable time frame. Approximation algorithms and heuristics are the most widely used approaches for obtaining reasonably accurate solutions to such hard problems. This subject introduces the basic concepts and techniques underlying these “inexact” approaches. We will address the following fundamental questions in the subject: How difficult is the problem under consideration? How closely can an optimal solution be approximated? And how can we go about finding near-optimal solutions in an efficient way? We will discuss methodologies for analysing the complexity and approximability of some important optimisation problems, including the travelling salesman problem, knapsack problem, bin packing, scheduling, network design, covering problems and facility location. We will also learn about various metaheuristics (simulated annealing, Tabu search, GRASP, genetic algorithms) and matheuristics (relax-and-fix, fix-and-optimise, local branching) that are widely used in solving real-world optimisation problems.
Intended learning outcomes
On completion of this subject, students should be able to:
- Explain the fundamental concepts and techniques underlying the design of inexact algorithms for discrete optimisation;
- Design and analyse approximation algorithms;
- Apply commonly used metaheuristics to complex optimisation problems;
- Demonstrate the application of approximation algorithms and heuristics in Python.
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: 31 January 2024
Eligibility and requirements
Prerequisites
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST30013 | Techniques in Operations Research | Semester 1 (Dual-Delivery - Parkville) |
12.5 |
Corequisites
None
Non-allowed subjects
None
Recommended background knowledge
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST30011 | Graph Theory | Semester 1 (Dual-Delivery - Parkville) |
12.5 |
Or:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST20018 | Discrete Maths and Operations Research | Semester 2 (Dual-Delivery - Parkville) |
12.5 |
Experience with basic computer programming.
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: 31 January 2024
Assessment
Description | Timing | Percentage |
---|---|---|
A written examination
| During the examination period | 70% |
Group project (groups of 3, equivalent to approximately 30 hours)
| Week 11 | 20% |
Written assignment (equivalent to approximately 10 hours)
| First half of the teaching period | 10% |
Additional details
This Dual-Delivery subject has On Campus assessment components.
Last updated: 31 January 2024
Dates & times
- Semester 2
Principal coordinator Charl Ras Mode of delivery Dual-Delivery (Parkville) Contact hours 36 hours comprising one 2-hour lecture per week and one 1-hour practical class per week. Total time commitment 170 hours Teaching period 26 July 2021 to 24 October 2021 Last self-enrol date 6 August 2021 Census date 31 August 2021 Last date to withdraw without fail 24 September 2021 Assessment period ends 19 November 2021 Semester 2 contact information
Time commitment details
170 hours
Additional delivery details
This Dual-Delivery subject has On Campus assessment components.
Last updated: 31 January 2024
Further information
- Texts
Prescribed texts
Juraj Hromkovic, Algorithmics for hard problems, Springer, 2002
Vijay V. Vazirani, Approximation Algorithms, Springer, 2003
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Science (Mathematics 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.
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: 31 January 2024