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FIT4010 Advanced topics in algorithms and discrete structures - Semester 1, 2011

Algorithms are the most fundamental area for all aspects of computer science and software engineering. Discrete structures, such as those treated in graph theory, set theory, combinatorics and symbolic logic form the mathematical underpinning of the study of algorithms. As well-designed algorithms and data structures are essential for the good performance of an information system, an in-depth understanding of the theoretical properties of algorithms is essential for any computer scientist. As importantly, the theoretical investigation of algorithms leads to a deeper understanding of problem structures and classes of problems and the knowledge of a large variety of algorithm types enables the designer to approach a new problem from different angles. Topics for this unit include: Computability and Complexity Automata Theory Advanced Analysis and Design of Algorithms Parallel and Distributed Algorithms Numerical Algorithms Cryptographic algorithms Spatial/geometric algorithms

Mode of Delivery

Clayton (Day)

Contact Hours

2 hrs lectures/wk, 1 hr laboratory or tutorial/wk


  • two hour lecture and
  • one hour tutorial (or laboratory) (requiring advance preparation)
  • a minimum of 3 hours of personal study per one hour of contact time in order to satisfy the reading and assignment expectations.

Unit Relationships


Completion of the Bachelor of Computer Science or equivalent to the entry requirements for the Honours program. Students must also have enrolment approval from the Honours Coordinator.

Chief Examiner

Kim Marriott

Campus Lecturer


Mark Carman

Contact hours: Tuesday 2pm - 3pm or make email appointment

Kim Marriott

Contact hours: Tuesday 2pm - 3pm or make email appointment

Learning Objectives

At the completion of this unit students will have:

  • an improved understanding of the issues involved in designing algorithms in the chosen specialisation area(s) and in analysing their performance;
  • an understanding of the mathematical formalisms that are relevant for these algorithms;
  • learned to recognise tasks that can be solved with these algorithms;
  • the ability to judge the limitations of these methods.With successful completion of the unit the students;
  • the ability to choose and apply algorithms and data structures in the chosen specialisation area(s);
  • the ability to evaluate the performance of algorithms using formal approaches;
  • the ability to design modified algorithms in the chosen area to suit particular problem structures.

Graduate Attributes

Monash prepares its graduates to be:
  1. responsible and effective global citizens who:
    1. engage in an internationalised world
    2. exhibit cross-cultural competence
    3. demonstrate ethical values
  2. critical and creative scholars who:
    1. produce innovative solutions to problems
    2. apply research skills to a range of challenges
    3. communicate perceptively and effectively

    Assessment Summary

    Assignment and Examination, relative weight depending on topic composition. When no exam is given students will be expected to demonstrate their knowledge by solving practical problems and maybe required to give an oral report. This variability is designed to give flexibility to the lecturer to decided the most appropriate form of examination for a given choice of topics.

    Assessment Task Value Due Date
    Assignment 1 - Modelling with MiniZinc 50% 21 April 2011
    Assignment 2 - Building an Automated Planning system using SAT, Heuristic Search and/or NLP techniques 50 % 27 May 2011

    Teaching Approach

    Lecture and tutorials or problem classes
    This teaching and learning approach provides facilitated learning, practical exploration and peer learning.


    Our feedback to You

    Types of feedback you can expect to receive in this unit are:
    • Informal feedback on progress in labs/tutes
    • Graded assignments with comments
    • Solutions to tutes, labs and assignments

    Your feedback to Us

    Monash is committed to excellence in education and regularly seeks feedback from students, employers and staff. One of the key formal ways students have to provide feedback is through SETU, Student Evaluation of Teacher and Unit. The University's student evaluation policy requires that every unit is evaluated each year. Students are strongly encouraged to complete the surveys. The feedback is anonymous and provides the Faculty with evidence of aspects that students are satisfied and areas for improvement.

    For more information on Monash's educational strategy, and on student evaluations, see:

    Previous Student Evaluations of this unit

    If you wish to view how previous students rated this unit, please go to

    Required Resources

    You will be using the MiniZinc modelling language.

    This is available from:

    Unit Schedule

    Week Date* Activities Assessment
    0 21/02/11   No formal assessment or activities are undertaken in week 0
    1 28/02/11 Introduction to constrained optimization  
    2 07/03/11 Modelling with MiniZinc Assignment 1 handed out
    3 14/03/11 Linear Programming  
    4 21/03/11 Mixed Integer Programming (MIP)  
    5 28/03/11 Network Simplex  
    6 04/04/11 Constraint Propagation (CP)  
    7 11/04/11 SAT techniques and planning applications  
    8 18/04/11 Heuristic search methods Assignment 1 due 21 April 2011
    Mid semester break
    9 02/05/11 Non-Linear Programming (NLP) Assignment 2 handed out
    10 09/05/11 Local and stochastic search methods  
    11 16/05/11 Tabu search and evolutionary methods  
    12 23/05/11 Research directions in constrained optimization Assignment 2 due 27 May 2011
      30/05/11   No formal assessment is undertaken SWOT VAC

    *Please note that these dates may only apply to Australian campuses of Monash University. Off-shore students need to check the dates with their unit leader.

    Assessment Policy

    To pass a unit which includes an examination as part of the assessment a student must obtain:

    • 40% or more in the unit's examination, and
    • 40% or more in the unit's total non-examination assessment, and
    • an overall unit mark of 50% or more.

    If a student does not achieve 40% or more in the unit examination or the unit non-examination total assessment, and the total mark for the unit is greater than 50% then a mark of no greater than 49-N will be recorded for the unit

    Assessment Tasks


    Students are expected to attend lectures and tutorials. However this is not mandatory.

    • Assessment task 1
      Assignment 1 - Modelling with MiniZinc
      In this assignment students will model a relatively simple constrained optimization problem using MiniZinc. They will be rquired to create models that work with a variety of different underlying solving techniques: MIP, CP and SAT.

      They will need to construct test data and then evaluate their models with this data.

      Produce a written report that describes their models, test data and the results of the evaluation. The report should also try and explain reasons for differences in behaviour of these models.
      Criteria for assessment:

      The quality of the models: correctness, efficiency, clarity and documentation.

      The quality of the test data: coverage.

      The quality of the written report including the quality of the evaluation and analysis of the differences in behaviour.

      Due date:
      21 April 2011
    • Assessment task 2
      Assignment 2 - Building an Automated Planning system using SAT, Heuristic Search and/or NLP techniques
      In this assignment students will investigate certain planning domains from the International Planning Competition. They will build a system to encode planning problems from that domain into SAT, Heuristic Search and / or non-linear programming formulations, which can then be solved using the aforementioned techniques.

      Students will need to show that their system works as desired (can discover reasonable plans) on a number of different problems (of increasing difficulty) from the chosen domain.

      Students will need to produce a written report describing their system and their evaluation of it.
      50 %
      Criteria for assessment:

      The quality of the planning system: its ability to find reasonable plans, its speed, and the type (complexity) of problems it can deal with.

      The quality of the written report including the quality of the evaluation and analysis.

      Due date:
      27 May 2011


    Assignment submission

    Assignment coversheets are available via "Student Forms" on the Faculty website:
    You MUST submit a completed coversheet with all assignments, ensuring that the plagiarism declaration section is signed.

    Extensions and penalties

    Returning assignments

    Resubmission of assignments

    Resubmission is not allowed unless special consideration applies in which case the course leaders may allow the student to resubmit an assignment.


    Monash has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University's academic standards, and to provide advice on how they might uphold them. You can find Monash's Education Policies at:

    Key educational policies include:

    Student services

    The University provides many different kinds of support services for you. Contact your tutor if you need advice and see the range of services available at The Monash University Library provides a range of services and resources that enable you to save time and be more effective in your learning and research. Go to or the library tab in portal for more information. Students who have a disability or medical condition are welcome to contact the Disability Liaison Unit to discuss academic support services. Disability Liaison Officers (DLOs) visit all Victorian campuses on a regular basis

    Reading List

    There are several recommended books for this subject:

    • Introduction to Mathematical Programming. W. L.  Winston. Duxbury Press, 1995.
    • Introduction to Operations Research. F.S. Hillier and G.J. Lieberman. McGraw-Hill, 8th Ed, 2005.
    • Constraint Programming - An Introduction. K. Marriott and P. Stuckey. MIT Press, 1998.
    • Numerical Optimization. J. Nocedal & S. Wright. Springer, 2006.
    • Automated Planning: Theory and Practice. D. Nau, M. Ghallab and P. Traverso. Morgan Kaufmann 2004.

    In addition to this, selected research papers will be referenced throughout the unit.
    The lecture material will be loosely based on this material and will be available through Moodle.