Course coordinator
Professor Claudio Mezzetti
Consultation TBA
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Economics School
This course begins extending elementary calculus concepts from ECON1050 to the analysis of functions of several variables. Then it covers convex multivariate optimisation. This is followed by further analysis on constrained optimisation. Finally, it provides essential elements of dynamic optimisation in discrete time. Applications include consumer problems, cost minimisation, and dynamic programming for dynamic economies.
This course is a first course in nonlinear optimization in economics, and introduces students to the corresponding mathematical results. It starts with classical optimization notions from calculus and proceeds to a study of convex functions. It deals with unconstrained and constrained optimization using Kuhn-Tucker conditions. Geometric intuition is developed.ﾠ
Course requirements
Assumed background
Students are expected to have successfully completed ECON1050 or approved equivalent.
Before attempting this course, you are advised that it is important to complete the appropriate prerequisite course(s) listed on the front of this course profile. No responsibility will be accepted by UQ School of Economics, the Faculty of Business, Economics and Law or The University of Queensland for poor student performance occurring in courses where the appropriate prerequisite(s) has/have not been completed, for any reason whatsoever.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
ECON1050 or MATH1051 or MATH1071
Course contact
School enquiries
Student Enquiries, School of Economics
All enquiries regarding student and academic administration (i.e. non-course content information, e.g., class allocation, timetables, extension to assessment due date, etc.) should be directed to enquiries@economics.uq.edu.au.
Enquiries relating specifically to course content should be directed to the econ2050@uq.edu.au.
Course staff
Lecturer
Professor Claudio Mezzetti
Tutor
Mr Ramazan Bora
Mr Harley Blackwood
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Tutorial Preferencing: Please refer to My Timetable (available via your my.UQ dashboard) for more information on the tutorial preferencing and allocation process. Tutorials will begin in Week 2 and students should attend one tutorial each week.ﾠ
Please note that during In-Semester Break (23-29 September 2024) we have no lectures of tutorials. The same applies for the Ekka holiday, week starting on Monday 12th of August.
The timetable is published through the UQ Public Timetable found in the APPs section of myUQ. Students should refer to the timetable prior to the commencement of classes to ensure that they have the most up to date information, as from time to time late room changes may occur.
Public Holidays: Wed 14 August (Royal Queensland Show), Mon 7 October (King's Birthday).
In-Semester Break: 23 - 29 September. Semester 2 classes recommence Mon 30 September. ﾠ
Aims and outcomes
This course aims to introduce students to nonlinear optimisation in several variables in economics; starting with classical notions from calculus and proceeding to a study of convex functions. It covers unconstrained optimisation problems, and constrained optimisation problems using the Kuhn-Tucker conditions. It concludes with a brief introduction to dynamic programming in discrete time. The course aspires to be rigorous (at the level of the prescribed textbook)ﾠ and to emphasizeﾠ the development of geometric intuition.ﾠ
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Demonstrate a clear understanding of basic topological concepts in Euclidean spaces.
LO2.
Apply key concepts from the calculus of several variables such as directional and total derivatives.
LO3.
Identify solutions of some unconstrained optimization problems.
LO4.
Identify solutions of some problems of constrained optimization with equality and with inequality contraints.
LO5.
Apply constrained and unconstrained optimization techniques to various economic questions.
LO6.
Apply dynamic programming in discrete time settings.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Tutorial/ Problem Set | Assignment 1 | 16% |
30/08/2024 5:00 pm |
| Tutorial/ Problem Set | Assignment 2 | 16% |
20/09/2024 5:00 pm |
| Tutorial/ Problem Set | Assignment 3 | 16% |
25/10/2024 5:00 pm |
| Examination |
Final exam
|
52% |
End of Semester Exam Period 2/11/2024 - 16/11/2024 |
Assessment details
Assignment 1
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 16%
- Due date
30/08/2024 5:00 pm
- Learning outcomes
- L01, L02
Task description
Assignment 1 comprises take-home problems on the content discussed in Lectures and Tutorials 1 - 4.
Incomplete answers may be awarded partial credit, provided that a substantial part of the problem is solved. This is decided by the course coordinator.
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
The assignment must be submitted electronically. Instuctions will be provided.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Extensions are limited to 7 calendar days to ensure timely feedback to other students.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Assignment 2
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 16%
- Due date
20/09/2024 5:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
Assignment 2 comprises take-home problems on the content discussed in Lectures and Tutorials 5 - 7.
Incomplete answers may be awarded partial credit, provided that a substantial part of the problem is solved. This is decided by the course coordinator.
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Extensions are limited to 7 calendar days to ensure timely feedback to other students.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Assignment 3
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 16%
- Due date
25/10/2024 5:00 pm
- Learning outcomes
- L03, L04, L05, L06
Task description
Assignment 3 comprises take-home problems on the content discussed in Lectures and Tutorials 8 - 10.
Incomplete answers may be awarded partial credit, provided that a substantial part of the problem is solved. This is decided by the course coordinator.
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
The assignment must be submitted electronically. Instructions will be provided.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Extensions are limited to 7 calendar days to ensure timely feedback to other students.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Final exam
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 52%
- Due date
End of Semester Exam Period
2/11/2024 - 16/11/2024
- Learning outcomes
- L01, L02, L03, L04, L05, L06
Task description
The final exam will:
- Consist of problem-solving and/or short-answer questions with a style similar to those in the tutorials and problem sets.
- Be Centrally Timetabled.
- Have further details about the exam posted on the course Blackboard site during the semester.
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) tools will not be permitted. Any attempted use of Generative AI may constitute student misconduct under the Student Code of Conduct.
Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | (In person) Casio FX82 series only or UQ approved and labelled calculator |
| Open/closed book | Closed Book examination - no written materials permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0% - 29% |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 30% - 46% |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47% - 49% |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50% - 64% |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65% - 74% |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75% - 84% |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85% - 100% |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
A student's final overall end of semester percentage mark will be rounded to determine their final grade. For example, 64.5% rounds to 65%, while 64.4% rounds to 64%.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Plagiarism
The School of Economics is committed to reducing the incidence of plagiarism. Further information on plagiarism and how to avoid an allegation of plagiarism is available in this course profile under Policies & Guidelines. Please refer to the Academic Integrity Module (AIM). It is strongly recommended that you complete the AIMﾠif you have not already done so.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Additional learning resources information
Additional material such as lecture slides and tutorial exercises will be posted on Blackboard.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 (22 Jul - 28 Jul) |
Lecture |
Lecture 1: Calculus of Several Variables, Part 1 Learning outcomes: L01 |
Week 2 (29 Jul - 04 Aug) |
Lecture |
Lecture 2: Calculus of Several Variables, Part 2 Learning outcomes: L01 |
Tutorial |
Tutorial 1: Calculus of Several Variables, Part 1 Learning outcomes: L01 |
|
Week 3 (05 Aug - 11 Aug) |
Lecture |
Lecture 3: Calculus of Several Variables, Part 3 Learning outcomes: L01, L02 |
Tutorial |
Tutorial 2: Calculus of Several Variables, Part 2 Learning outcomes: L01 |
|
Week 4 (12 Aug - 18 Aug) |
No student involvement (Breaks, information) |
Wednesday 14/08/2024 Royal QLD Show Public Holiday No lectures or tutorials this week. |
Week 5 (19 Aug - 25 Aug) |
Lecture |
Lecture 4: Calculus of Several Variables, Part 4 Learning outcomes: L01, L02 |
Tutorial |
Tutorial 3: Calculus of Several Variables, Part 3 Learning outcomes: L01, L02 |
|
Week 6 (26 Aug - 01 Sep) |
Lecture |
Lecture 5: Unconstrained optimization, Part 1 Learning outcomes: L01, L02, L03, L05 |
Tutorial |
Tutorial 4: Calculus of Several Variables, Part 4 Learning outcomes: L01, L02 |
|
Week 7 (02 Sep - 08 Sep) |
Lecture |
Lecture 6: Unconstrained optimization, Part 2 Learning outcomes: L01, L02, L03, L05 |
Tutorial |
Tutorial 5: Unconstrained optimization, Part 1 Learning outcomes: L01, L02, L03, L05 |
|
Week 8 (09 Sep - 15 Sep) |
Lecture |
Lecture 7: Unconstrained Optimization, Part 3 Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Tutorial 6: Unconstrained Optimization, Part 2 Learning outcomes: L01, L02, L03, L05 |
|
Week 9 (16 Sep - 22 Sep) |
Lecture |
Lecture 8: Constrained Optimization, Part 1 Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Tutorial 7:: Unconstrained Optimization, Part 3 Learning outcomes: L01, L02, L03, L04, L05 |
|
Mid Sem break (23 Sep - 29 Sep) |
No student involvement (Breaks, information) |
Mid Sem break No lectures and no tutorials this week. |
Week 10 (30 Sep - 06 Oct) |
Lecture |
Lecture 9: Constrained Optimization, Part 2 Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Tutorial 8: Constrained Optimization, Part 1 Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 11 (07 Oct - 13 Oct) |
Lecture |
Lecture 10: Constrained Optimization, Part 3 Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Tutorial 9: Constrained Optimization, Part 2 Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 12 (14 Oct - 20 Oct) |
Lecture |
Lecture 11: Dynamic Programming Learning outcomes: L01, L02, L03, L04, L05, L06 |
Tutorial |
Tutorial 10: Constrained Optimization, Part 3 Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 13 (21 Oct - 27 Oct) |
Lecture |
Lecture 12: Review |
Tutorial |
Tutorial 11: Dynamic Programming Learning outcomes: L01, L02, L03, L04, L05 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.