mat5opt optimisation
OPTIMISATION
MAT5OPT
2020
Credit points: 15
Subject outline
Optimisation is the process of maximizing or minimizing some objective of interest, while satisfying constraints. Optimisation problems are fundamental and ubiquitous in the study of machine learning, signal processing, and statistics. This subject will develop the mathematical theory, introduce useful tools, and explain the algorithms and their implementation. A variety of distinct optimisation problem types will be encountered including linear, quadratic, and integer programming, as well as various unconstrained problems. Real world instances of such problems will be considered, and solution methods for such problems will be studied. A basic knowledge of calculus and real analysis is assumed.
SchoolEngineering and Mathematical Sciences
Credit points15
Subject Co-ordinatorPeter Van Der Kamp
Available to Study Abroad/Exchange StudentsNo
Subject year levelYear Level 5 - Masters
Available as ElectiveNo
Learning ActivitiesN/A
Capstone subjectNo
Subject particulars
Subject rules
Prerequisites Must be admitted in one of the following courses: SHS (in mathematics, statistics) or SHCS or SMDS or SMENM
Co-requisitesN/A
Incompatible subjectsN/A
Equivalent subjectsN/A
Quota Management StrategyN/A
Quota-conditions or rulesN/A
Special conditionsN/A
Minimum credit point requirementN/A
Assumed knowledgeN/A
Career Ready
Career-focusedNo
Work-based learningNo
Self sourced or Uni sourcedN/A
Entire subject or partial subjectN/A
Total hours/days requiredN/A
Location of WBL activity (region)N/A
WBL addtional requirementsN/A
Graduate capabilities & intended learning outcomes
Graduate Capabilities
Intended Learning Outcomes
Subject options
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Melbourne (Bundoora), 2020, Semester 1, Blended
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorPeter Van Der Kamp
Class requirements
Directed ReadingWeek: 10 - 22
One 2.00 hours directed reading per week on any day including weekend during the day from week 10 to week 22 and delivered via online.
Readings and video clips
PracticalWeek: 10 - 22
One 2.00 hours practical per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
Classes will be in a computer lab
Assessments
| Assessment element | Category | Contribution | Hurdle | % | ILO* |
|---|---|---|---|---|---|
| Sets of mathematical problems (each equiv. to 800 words) Assignments are sets of problems, where emphasis is placed on implementation of algorithms and use of software tools. | N/A | N/A | No | 30 | SILO1, SILO2, SILO3 |
| One written investigation (equiv. to 1600 words) The written investigation is a literature study/review or an in-depth treatment of a real world optimisation problem. | N/A | N/A | No | 20 | SILO2, SILO3, SILO4 |
| One 2 hour exam (2000 words equivalent) The exam is a set of problems, where emphasis is placed on modelling, concepts, and theories. | N/A | N/A | No | 50 | SILO1, SILO2, SILO4 |