APP MTH 7072 - Optimisation

North Terrace Campus - Semester 1 - 2020

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.

  • General Course Information
    Course Details
    Course Code APP MTH 7072
    Course Optimisation
    Coordinating Unit Mathematical Sciences
    Term Semester 1
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange Y
    Assumed Knowledge MATHS 1012 and knowledge of linear programming such as would be obtained from APP MTH 2105 and basic computer programming skills such as would be obtained from ENG 1002, 1003, COMP SCI 1012, 1101, MECH ENG 1100, 1102, 1103, 1104, 1105, C&ENVENG 1012
    Assessment Ongoing assessment, exam
    Course Staff

    Course Coordinator: Professor Matthew Roughan

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    1. Understand the complexities of, and techniques for solving, nonlinear optimisation problems.
    2. Apply suitable algorithms to one- or multi-dimensional optimisation problems.
    3. Understand the theoretical framework underlying the techniques presented in class.
    4. Implement computer code for the algorithms as studied in class and critically analyse and interpret the results.
    5. Demonstrate skills in communicating mathematics orally and in writing.
    6. Demonstrate the ability to work in a group applying the theory developed in this 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)
    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
    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
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    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
    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
  • Learning Resources
    Required Resources
    Access to the intranet.
    Recommended Resources
    Edwin K.P. Chong and Stanislaw H. Zak. An Introduction to Optimization. 3rd edition. John Wiley & Sons, 2008. doi: 10.1002/9781118033340
    Online Learning
    All assignments, tutorials, handouts and solutions, where appropriate, will be made available on MyUni as the course progresses.Recordings of lectures will be available on MyUni following each lecture.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    The 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.

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    ActivityQuantityWorkload Hours
    Lectures 30 90
    Homework TBD 16
    Assignments 5 30
    Tutorials 5 20
    Total 156
    Learning Activities Summary
    1. Single variable optimisation: Introduction, Dichotomous and Golden section searches, unbounded searches, Quadratic approximation, Newton's and secant methods
    2. 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
    3. Constrained convex optimisation: introduction to constrained optimisation, Linear constraints, Lagrange multipliers, KKT conditions, Generalisations of KKT conditions, orthogonal projection, Gradient Projection algorithm
    4. Non-convex optimisation: introduction to non-convex optimisation; methods from Genetic Algorithms, Simulated Annealing, Monte Carlo optimisation
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Assessment task Task type Weighting Learning outcomes
    Examination Summative 70% All
    Assignments, regular homework Formative and summative 30% All
    Assessment Related Requirements
    An aggregate score of at least 50% is required to pass the course.
    Assessment Detail
    Assessment itemDistributedDue dateWeighting
    Assignment 1 Week 2 Week 3 5%
    Assignment 2 Week 4 Week 5 5%
    Assignment 3 Week 6 Week 7 5%
    Assignment 4 Week 9 Week 10 5%
    Assignment 5 Week 11 Week 13 5%
    Regular Homework ongoing ongoing 5%
    1. 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.
    2. Late assignments will require a request prior to the due date, and a medical certificate or other documentation.
    3. Assignments normally have a one week turn-around time for feedback to students.
    Course Grading

    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

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy ( course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
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