Note: This is an archived Handbook entry from 2015.
|Dates & Locations:|| |
This subject has the following teaching availabilities in 2015:Semester 2, Parkville - Taught on campus.
Timetable can be viewed here. For information about these dates, click here.
|Time Commitment:||Contact Hours: 36 hours, comprising of two 1-hour lectures and one 1-hour workshop per week |
Total Time Commitment:
One of the following:
Study Period Commencement:
Semester 1, Semester 2
Not offered in 2015
|Recommended Background Knowledge:|| |
|Non Allowed Subjects:|| |
433-433 Constraint Programming
|Core Participation Requirements:||
For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.
It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability
CoordinatorProf Peter Stuckey
The aims for this subject is for students to develop an understanding of approaches to solving combinatorial optimization problems with computers, and to be able to demonstrate proficiency in modelling and solving programs using a high-level modelling language, and understanding of different solving technologies. The modelling language used is MiniZinc.
Topics covered will include:
INTENDED LEARNING OUTCOMES (ILO)
On completion of this subject the student is expected to:
Hurdle requirement: To pass the subject, students must obtain at least:
Intended Learning Outcomes (ILOs) 1, 2, 3, and 4 are addressed in the lectures, laboratory exercises, project assignments and the end-of-semester examination
|Prescribed Texts:|| |
|Breadth Options:|| |
This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
On completion of this subject students should be able to have the following skills:
LEARNING AND TEACHING METHODS
The subject comprises a weekly 2 hour lecture followed by a 1 hour laboratory exercise. Weekly readings are assigned from the textbook, and laboratory exercises are assigned. Additionally, a significant amount of project work is assigned.
INDICATIVE KEY LEARNING RESOURCES
At the beginning of the year, the coordinator will propose a textbook on constraint programming and will be made available through University Book Shop and library. The current suggested textbook is
Programming with Constraints: an Introduction. Kim Marriott and Peter J. Stuckey, MIT Press. 1998.
CAREERS / INDUSTRY LINKS
The IT industry is a large and steadily growing industry. Increasingly companies are seeking to use optimization technology to provide decision support, assist in strategic and tactical planning, and manage daily operations. Modelling skills and understanding of optimization technology are essential for working in the optimization industry, for example in optimization consulting companies, or within the strategic planning groups within any major company. Most large companies have many problems that require optimization technology to be solved. Modelling and solving skills are also vital for employees whose role is to tackle these problems.
Master of Information Technology |
Master of Information Technology
Master of Philosophy - Engineering
Master of Science (Computer Science)
Approved Masters level subjects from other departments |
B-ENG Software Engineering stream
MIT Computing Specialisation
MIT Distributed Computing Specialisation
Master of Engineering (Software)
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