APP MTH 3014 - Optimisation III
North Terrace Campus - Semester 1 - 2019
General Course Information
Course Code APP MTH 3014 Course Optimisation III Coordinating Unit School of Mathematical Sciences Term Semester 1 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Y Prerequisites MATHS 1012 Assumed Knowledge Knowledge of linear programming such as would be obtained from APP MTH 2105 and basic computer programming skills such as would be obtained from COMP SCI 1012, 1101, MECH ENG 1100, 1102, 1103, 1104, 1105, C&ENVENG 1012 Course Description Most problems in life are optimisation problems: what is the best design for a racing kayak? How do you get the best return on your investments? What is the best use of your time in swot vac? What is the shortest route across town for an emergency vehicle? What are the optimal release rates from a dam for environmental flows in a river? Mathematical formulations of such optimisation problems might contain one or many independent variables. There may or may not be constraints on those variables. However, there is always an objective: minimise or maximise some function of the variable(s) subject to the constraints. This course examines nonlinear mathematical formulations, and concentrates on convex optimisation problems. Many modern optimisation methods in areas such as design of communication networks and finance rely on the classical underpinnings covered in this course. Topics covered are: One-dimensional (line) searches, direct methods, polynomial approximation, methods for differentiable functions; Theory of convex and nonconvex functions relevant to optimisation; Multivariable unconstrained optimisation, quasi-Newton Method, steepest descent methods, conjugate gradient methods; Constrained optimisation, including Karush-Kuhn-Tucker conditions and the Gradient Projection Method; Heuristics for non-convex problems, genetic algorithms.
No information currently available.
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes
- Understand the complexities of, and techniques for solving, nonlinear optimisation problems.
- Apply suitable algorithms to one- or multi-dimensional optimisation problems.
- Understand the theoretical framework underlying the techniques presented in class.
- Implement computer code for the algorithms as studied in class and critically analyse and interpret the results.
- Demonstrate skills in communicating mathematics orally and in writing.
- Demonstrate the ability to investigate and analyse material related to the course
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1,2,3,4 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
1,3,4,5,6 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
5,6 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
1,5,6 Intercultural and ethical competency
- adept at operating in other cultures
- comfortable with different nationalities and social contexts
- Able to determine and contribute to desirable social outcomes
- demonstrated by study abroad or with an understanding of indigenous knowledges
5,6 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
Required ResourcesAccess to the intranet.
Recommended ResourcesEdwin K.P. Chong and Stanislaw H. Zak. An Introduction to Optimization. 3rd edition. John Wiley & Sons, 2008. doi: 10.1002/9781118033340
Online LearningAll assignments, tutorials, handouts and solutions, where appropriate, will be made available on MyUni as the course progresses.
Recordings of lectures will generally be available on MyUni following each lecture.
Learning & Teaching Activities
Learning & Teaching ModesThe lecturer guides the students through the course material in 36 classes. Students are to engage with the material in the classes and in private study. Interaction with the lecturer and discussion of any difficulties that arise during the lecture is encouraged. Students are expected to participate in all lectures. Frequent small homework assignments will promote staged active learning. Fortnightly assignments help students strengthen their understanding of the theory and their skills in applying it, and allow them to gauge their progress. Small open-ended projects aim to facilitate developing investigative and integrative analytical skills.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload hours Lectures 36 90 Homework TBA 18 Assignments 6 48 Total 156
Learning Activities Summary
- Single variable optimisation: Introduction, Dichotomous and Golden section searches, unbounded searches, Quadratic approximation, Newton's and secant methods
- Unconstrained multi-variable optimisation: introduction to unconstrained problems, Levenberg--Marquardt method, convexity, theorems for minimality and descent methods, Steepest descent on quadratics, Conjugate gradient method, Fletcher-Reeves algorithm
- Constrained convex optimisation: introduction to constrained optimisation, Linear constraints, Lagrange multipliers, KKT conditions, Generalisations of KKT conditions, orthogonal projection, Gradient Projection algorithm
- Non-convex optimisation: introduction to non-convex optimisation; methods from Genetic Algorithms, Simulated Annealing, Monte Carlo optimisation
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment task Task type Weighting Learning outcomes Examination Summative 70% All Assignments, homework, project Formative and summative 30% All
Assessment Related RequirementsAn aggregate score of at least 50% is required to pass the course.
Assessment Item Distributed Due Date Weighting Assignment 1 Week 2 Week 3 4% Assignment 2 Week 4 Week 5 4% Assignment 3 Week 6 Week 7 4% Assignment 4 Week 8 Week 9 4% Assignment 5 Week 10 Week 11 4% Assignment 6 Week 12 Week 13 4% Homework / Project ongoing ongoing 6%
- All written assignments are to be either submitted to the designated hand in boxes within the School of Mathematical Sciences with a signed cover sheet attached, or submitted as pdf via MyUni.
- Late assignments will require a request prior to the due date, and a medical certificate or other documentation.
- Assignments normally have a one week turn-around time for feedback to students.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
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