• Class Number 7403
  • Term Code 3260
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
  • COURSE CONVENER
    • Dr Jose Pedro Barreira Iria
  • LECTURER
    • Ahmad Attarha
    • Dr Alban Grastien
    • Dr Jose Pedro Barreira Iria
  • Class Dates
  • Class Start Date 25/07/2022
  • Class End Date 28/10/2022
  • Census Date 31/08/2022
  • Last Date to Enrol 01/08/2022
SELT Survey Results

This course provides foundations and plenty of exercises in practical optimisation problems, while covering all basic elements of optimisation including forms of constraint programming as well as variations on linear programming and convex optimisation.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Be able to apply Linear Programming and Mixed-Integer Programming model to solve real-world problems.
  2. Be able to recognize and formulate convex optimization problems arising in practice.
  3. Demonstrate an understanding of theoretical foundations of convex optimization and be able to use it to characterize optimal solutions to general problems.
  4. Be able to define an appropriate local search neighbourhood for a given problem.
  5. Be able to use and reflect on a variety of meta-heuristics to escape local minima in a neighbourhood.
  6. Demonstrate an understanding and reflect on of the propagation of a global constraint in a Constraint programming system.

Whether you are on campus or studying remotely, there are a variety of online platforms you will use to participate in your study program. These could include videos for lectures and other instruction, two-way video conferencing for interactive learning, email and other messaging tools for communication, interactive web apps for formative and collaborative activities, print and/or photo/scan for handwritten work and drawings, and home-based assessment.

ANU outlines recommended student system requirements to ensure you are able to participate fully in your learning. Other information is also available about the various Learning Platforms you may use.

Staff Feedback

Students will be given feedback in the following forms in this course:

  • written comments
  • verbal comments
  • feedback to whole class, groups, individuals, focus group etc

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.

Other Information

Please see the course website for more detailed information.

Class Schedule

Week/Session Summary of Activities Assessment
1 Lecture Material (see course page)
2 Lecture Material, Labs (see course page)
3 Lecture Material, Labs (see course page)
4 Lecture Material, Labs (see course page) Assignment 1 Due
5 Lecture Material, Labs (see course page)
6 Lecture Material, Labs (see course page) Assignment 2 Due
7 Lecture Material, Labs (see course page)
8 Lecture Material, Labs (see course page) Assignment 3 Due
9 Lecture Material, Labs (see course page)
10 Lecture Material, Labs (see course page) Assignment 4 Due
11 Lecture Material, Labs (see course page)
12 Guest Lectures, Labs (see course page) Seminar Videos Due

Assessment Summary

Assessment task Value Due Date Learning Outcomes
Final Exam 50 % * 1, 2, 3, 4, 5, 6
Assignment 1: Linear programming 8 % 19/08/2022 1, 2, 3
Assignment 2: Mixed-integer linear programming 8 % 02/09/2022 1
Assignment 3: MiniZinc 8 % 30/09/2022 4, 6
Assignment 4: Local Search and Metaheuristics 8 % 14/10/2022 4, 5, 6
Lab and Forum Activities 6 % * 1, 2, 3, 4, 5, 6
Seminar Presentation 10 % 28/10/2021 1, 2, 3, 4, 5, 6

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details

Policies

ANU has educational policies, procedures and guidelines , which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Integrity Rule before the commencement of their course. Other key policies and guidelines include:

Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the Academic Skills website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.

Assessment Task 1

Value: 50 %
Learning Outcomes: 1, 2, 3, 4, 5, 6

Final Exam

Brief description: Computer-based final exam.

Mark: 50% of the overall mark (40% hurdle to pass the course).

Assessment Task 2

Value: 8 %
Due Date: 19/08/2022
Learning Outcomes: 1, 2, 3

Assignment 1: Linear programming

Brief description: Formulation and implementation of the linear program described in the assignment.

Deliverables: A document with the answers and a python file with the implementation.

Mark: 8.5% of the overall mark. Note that the assessment summary table can not handle fractional values.

Released: week 2.

Due: week 4.

Assessment Task 3

Value: 8 %
Due Date: 02/09/2022
Learning Outcomes: 1

Assignment 2: Mixed-integer linear programming

Brief description: Formulation and implementation of the mixed-integer linear program described in the assignment.

Deliverables: A document with the answers and a python file with the implementation.

Mark: 8.5% of the overall mark. Note that the assessment summary table can not handle fractional values.

Released: week 4.

Due: week 6.

Assessment Task 4

Value: 8 %
Due Date: 30/09/2022
Learning Outcomes: 4, 6

Assignment 3: MiniZinc

Brief description: Implementation of the optimisation problem described in the assignment.

Deliverables: A MiniZinc file with the implementation.

Mark: 8.5% of the overall mark. Note that the assessment summary table can not handle fractional values.

Released: week 6.

Due: week 8.

Assessment Task 5

Value: 8 %
Due Date: 14/10/2022
Learning Outcomes: 4, 5, 6

Assignment 4: Local Search and Metaheuristics

Brief description: Formulation and implementation of the metaheuristic problem described in the assignment.

Deliverables: A document with the answers and python files with the implementation.

Mark: 8.5% of the overall mark. Note that the assessment summary table can not handle fractional values.

Released: week 8.

Due: week 10.

Assessment Task 6

Value: 6 %
Learning Outcomes: 1, 2, 3, 4, 5, 6

Lab and Forum Activities

5% for lab activities: discussing and developing lab exercises with tutors.

1% for asking or answering another student's question on piazza, or asking a question in a drop-in session. Must be done in the first three weeks of the semester.

Assessment Task 7

Value: 10 %
Due Date: 28/10/2021
Learning Outcomes: 1, 2, 3, 4, 5, 6

Seminar Presentation

Brief description: Small group project on an optimisation algorithm or application. The groups will study the topic and present it to the class in the form of a video recording or video presentation.

Deliverables: Video recording or live presentation.

Mark: 10% of the overall mark.

Released: week 1.

Group and Topic Selection: week 7.

Due: week 12.

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.


The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.


The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.

 

The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.

Online Submission

You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.

Hardcopy Submission

For some forms of assessment (hand written assignments, art works, laboratory notes, etc.) hard copy submission is appropriate when approved by the Associate Dean (Education). Hard copy submissions must utilise the Assignment Cover Sheet. Please keep a copy of tasks completed for your records.

Late Submission

Individual assessment tasks may or may not allow for late submission. Policy regarding late submission is detailed below:

  • Late submission not permitted. If submission of assessment tasks without an extension after the due date is not permitted, a mark of 0 will be awarded.

Referencing Requirements

The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

Dr Jose Pedro Barreira Iria
u1091356@anu.edu.au

Research Interests


Dr Jose Pedro Barreira Iria

By Appointment
Ahmad Attarha
0474449685
Ahmad.Attarha@anu.edu.au

Research Interests


Ahmad Attarha

Dr Alban Grastien
Alban.Grastien@anu.edu.au

Research Interests


Dr Alban Grastien

Dr Jose Pedro Barreira Iria
Jose.Iria@anu.edu.au

Research Interests


Dr Jose Pedro Barreira Iria

By Appointment

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions