Introduction to Optimisation

Subject ELEN90026 (2016)

Note: This is an archived Handbook entry from 2016.

Credit Points: 12.5
Level: 9 (Graduate/Postgraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2016:

Semester 2, Parkville - Taught on campus.
Pre-teaching Period Start not applicable
Teaching Period 25-Jul-2016 to 23-Oct-2016
Assessment Period End 18-Nov-2016
Last date to Self-Enrol 05-Aug-2016
Census Date 31-Aug-2016
Last date to Withdraw without fail 23-Sep-2016

Timetable can be viewed here. For information about these dates, click here.
Time Commitment: Contact Hours: 36 hours of lectures
Total Time Commitment:

200 hours


Enrolment in a research higher degree (MPhil or PhD) in Engineering



Recommended Background Knowledge:


Non Allowed Subjects:


Core Participation Requirements:

For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Students Experiencing Academic Disadvantage Policy, academic requirements for this subject are articulated in the Subject Description, Subject Objectives, Generic Skills and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the disability support scheme can be found at the Disability Liaison Unit website:


Dr Iman Shames


Subject Overview:


This subject provides a rigorous introduction to the mathematics of optimization, as used across all of science and particularly in engineering design. There is an emphasis on both the theory and application of optimization techniques, with a focus on fundamental areas such as convex optimization and/or discrete optimization. This subject is intended for research higher-degree students in engineering.


Topics may include:

  • Convex sets and functions
  • Convex optimization problems
  • Duality theory
  • Algorithms for unconstrained optimization
  • Algorithms for constrained optimization
  • Discrete and combinatorial optimization
  • Computational complexity
  • Approximation algorithms.

Learning Outcomes:


Having completed this subject it is expected that the student be able to:

1. Demonstrate an in-depth understanding of convex or discrete analysis within the context of optimization problems

2. Formulate and solve engineering problems via convex or discrete optimization methods

3. Apply computational tools to solve standard convex or discrete optimization problems.

  1. Continuous assessment of homework assignments, not exceeding 40 pages in total over the semester (approximately 55-60 hours of work), worth 40%
  2. Final examination at the end of semester, worth 60% (ILO's 1 - 3).

Hurdle requirement: Students must pass the written exam to pass the subject.

Prescribed Texts:


Breadth Options:

This subject is not available as a breadth subject.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
Generic Skills:

On completion of this subject, students will have developed the following skills:

  • Ability to apply knowledge of basic science and engineering fundamentals;
  • In-depth technical competence in at least one engineering discipline;
  • Ability to undertake problem identification, formulation and solution;
  • Ability to utilise a systems approach to design and operational performance;
  • Expectation of the need to undertake lifelong learning, capacity to do so;
  • Capacity for independent critical thought, rational inquiry and self-directed learning;
  • Profound respect for truth and intellectual integrity, and for the ethics of scholarship.


The subject is delivered through lectures and homework assignments


Students are provided with lecture notes, including worked examples, assignment problems, and recommended reading lists comprising textbooks and journal articles.


Exposure to research literature and the rigour expected at the level of postgraduate study.

Related Course(s): Doctor of Philosophy - Engineering
Master of Philosophy - Engineering

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