OPTIMISATION

MAT5OPT

2021

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

01. Translate real-world problems into mathematical form, using the language of optimisation theory.
02. Synthesise information, concepts and theories of unconstrained optimisation, and of linear, quadratic, and integer programming.
03. Employ tools and implement solution methods and algorithms for unconstrained optimisation, and linear, quadratic, and integer programming.
04. Apply optimisation techniques to a range of practical problems.

Subject options

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Start date between: and    Key dates

Melbourne (Bundoora), 2021, 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 h 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 h 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 elementCommentsCategoryContributionHurdle% 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/AN/AN/ANo30 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/AN/AN/ANo20 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/AN/AN/ANo50 SILO1, SILO2, SILO4