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Approximation Algorithms and Heuristics (MAST90098)
Graduate courseworkPoints: 12.5Not available in 2019
Overview
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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
After completing this subject, students will:
- understand the fundamental concepts and techniques underlying the design of inexact algorithms for discrete optimisation;
- have developed the skills needed to design and analyse approximation algorithms;
- know how to apply some commonly used metaheuristics to complex optimisation problems;
- have gained experience in implementing 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: 3 November 2022
Eligibility and requirements
Prerequisites
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST30013 | Techniques in Operations Research | Semester 1 (On Campus - Parkville) |
12.5 |
Corequisites
None
Non-allowed subjects
None
Recommended background knowledge
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST30011 | Graph Theory | Semester 1 (On Campus - Parkville) |
12.5 |
Or:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST20018 | Discrete Maths and Operations Research | Semester 2 (On Campus - 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: 3 November 2022
Assessment
Additional details
A 3-hour written examination in the examination period (60%). 4 assignments of up to 50 pages in total worth 10% each due mid to late semester (40%).
Last updated: 3 November 2022
Dates & times
Not available in 2019
Time commitment details
170 hours
Last updated: 3 November 2022
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: 3 November 2022