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Unit of study_

BUSS4932: Advanced Optimisation for Business

Semester 1, 2024 [Normal day] - Camperdown/Darlington, Sydney

This unit covers advanced research-integrated coursework topics in optimisation and stochastic processes, such as convex optimisation, duality, approximation, statistical estimation, random walks and Markov chains, and Poisson and other stochastic processes. The theory is complemented with relevant business examples allowing students to gain a deep understanding of the models and the ability to tailor them to various business applications.

Unit details and rules

Unit code BUSS4932
Academic unit Business Analytics
Credit points 6
Prohibitions
? 
None
Prerequisites
? 
Students must meet the entry requirements for the Bachelor of Advanced Studies (Advanced Coursework), including completion of a pass undergraduate degree and a major in Business Analytics (including QBUS3600)
Corequisites
? 
None
Assumed knowledge
? 

Students are expected to be familiar with all aspects of Business Analytics, including Optimisation, Regression Modelling, Statistical Modelling and Machine Learning

Available to study abroad and exchange students

No

Teaching staff

Coordinator Nam Ho-Nguyen, nam.ho-nguyen@sydney.edu.au
Type Description Weight Due Length
Assignment Research Project
Students will explore recent research and applications.
40% Please select a valid week from the list below
Due date: 24 May 2024 at 23:59

Closing date: 27 May 2024
Semester-long
Outcomes assessed: LO1 LO2 LO3
Assignment Assignment 1
Assignment 1
20% Week 04
Due date: 15 Mar 2024 at 23:59

Closing date: 29 Mar 2024
Three weeks
Outcomes assessed: LO1 LO3
Assignment Assignment 2
Assignment 2
20% Week 07
Due date: 12 Apr 2024 at 23:59

Closing date: 26 Apr 2024
Three weeks
Outcomes assessed: LO1 LO3
Assignment Assignment 3
Assignment 3
20% Week 10
Due date: 03 May 2024 at 23:59

Closing date: 17 May 2024
Three weeks
Outcomes assessed: LO1 LO3

Assessment summary

Assignments (60% of total mark). There will be three assignments assessing technical concepts taught in lectures. Solutions must be typeset in LaTeX. Marks will beallocated for correctness as well as writing style.

Research Project (40% of total mark). Students will explore a theoretical or practical application of convex optimisation, and conduct a computational study. Students will present findings from their project in class and provide a written report. Submissions must be typeset in LaTeX.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school. 

Distinction

75 - 84

Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Credit

65 - 74

Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Pass

50 - 64

Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school. 

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Late assessments will not be accepted unless there are extenuating circumstances.

Academic integrity

The Current Student website  provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.  

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

WK Topic Learning activity Learning outcomes
Week 01 Convex sets and functions Lecture and tutorial (4 hr) LO1
Week 02 Operations preserving convexity Lecture and tutorial (4 hr) LO1
Week 03 Quadratics and positive semidefinite matrices Lecture and tutorial (4 hr) LO1
Week 04 Gradients and subgradients Lecture and tutorial (4 hr) LO1 LO3
Week 05 Convex optimisation Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 06 Lagrange duality Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 07 Saddle point problems Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 08 Semidefinite relaxations of nonconvex quadratic optimisation Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 09 Conic programming Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 10 Conic programming duality Lecture and tutorial (4 hr) LO1 LO3
Week 11 Student presentations Presentation (4 hr) LO1 LO2 LO3
Week 12 Student presentations Presentation (4 hr) LO1 LO2 LO3
Week 13 Student presentations Presentation (4 hr) LO1 LO2 LO3

Attendance and class requirements

Students are expected to attend all lectures and tutorials.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

S. Boyd and L. Vandenberghe, Convex Optimization. The book is freely available online.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. Recognise/formulate problems as convex optimization problems
  • LO2. Develop code for problems of moderate size
  • LO3. Characterize optimal solution, discuss limits of the performance, suggest creative solutions to overcome limits in order to compute the optimal solution

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

Minor changes to assessment structure, to better align student effort and mark allocation.
  • If you're looking for something to do before class starts, you could read Chapter 1 of the textbook.

  • You will use CVXPY to write simple scripts, so basic familiarity with elementary Python programming is required. We will not be supporting other packages for convex optimization, such as Convex.jl (Julia), CVX (Matlab), and CVXR (R). In particular, the final exam will require the use of CVXPY

  • You will use LaTeX to typeset homework assignments. So, basic familiarity with LaTeX is required. You can see the introduction here.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.