FIT1029 Algorithmic problem solving - Semester 1, 2013

Algorithms are recipes for solving a problem. They are fundamental to computer science and software engineering. Algorithms are the formal foundation of computer programming but also exist independently of computers as systematic problem-solving procedures. This unit introduces algorithmics, the study of algorithms. It is not about programming and coding but rather about understanding and analysing algorithms and about algorithmic problem-solving, i.e. the design of systematic problem-solving procedures. The unit will not require any knowledge of a programming language and is very hands-on. Students will develop algorithms to solve a wide variety of different problems, working individually as well as together in groups and as a class.

Topics include: what is a computational problem and what is an algorithm; basic control structures; basic data structures; modular algorithm structure; recursion; problem-solving strategies for algorithm development; arguing correctness of an algorithm; arguing termination of an algorithm; understanding the efficiency of an algorithm; and limitations of algorithms.

Mode of Delivery

• Clayton (Day)
• Sunway (Day)

Contact Hours

2 hrs lectures/wk, 2 hrs tutorials/wk

Workload requirements

Students will be expected to spend 12 hours per week on various activities including:

• two-hour lecture and
• two-hour tutorial  (requiring advance preparation)
• a minimum of 2-3 hours of personal study per one hour of contact time in order to satisfy the reading and assignment expectations.

• You will need to allocate up to 2 hours per week in some weeks, for use of a computer, including time for newsgroups/discussion groups.

Chief Examiner

Campus Lecturer

Academic Overview

Learning Outcomes

At the completion of this unit students will have -A knowledge and understanding of:

• the difference between algorithms and processes;
• basic ways to structure algorithms: basic data structures (simple variables, collections structure, specifically vectors, lists, sets, and tables); basic control structures (sequence, choice, iteration);
• recursion;
• modular algorithm structures;
• the equivalence of recursion and iteration;
• problem solving strategies suitable for algorithm development including top-down design and bottom-up design;
• simple standard patterns for algorithms (eg traversal, search);
• what makes a good algorithm
• limitations of algorithms (high level).

Developed the skills to:

• develop simple iterative and recursive algorithms
• argue the correctness of simple algorithms
• judge the efficiency of simple algorithms, and

Developed attitudes that enable them to:

• value clear specification of problems;
• understand the relation between algorithms and programs;
• appreciate the value of designing abstract algorithms before starting to code a program;
• have confidence that they can develop algorithms to solve computational problems;
• appreciate that seemingly difficult problems can have very simple elegant algorithmic solutions (and vice versa);
• value correctness arguments for algorithms; and
• value the importance of simplicity and efficiency.

Demonstrated the communication skills necessary to:

• solve a problem by discussing possible approaches and solutions as a team; and
• clearly communicate (the specification of) a computational problem, its algorithmic solution and arguments for correctness and efficiency.

Unit Schedule

Week	Activities	Assessment
0		No formal assessment or activities are undertaken in week 0
1	Introduction to the unit and the type of problems
2	Understanding and modelling the problem
3	Invariants in problems and data
4	Decomposition of problems and applying Brute Force to solve problems	Assignment 1 due
5	Using abstraction, symmetry, heuristics and divide and conquer to simplify problems
6	Recursion	Test during lecture
7	Backtracking
8	Dynamic Programming
9	Fundamentals
10	Abstract Data Types and Correctness	Assignment 2 due
11	Complexity
12	Limitations of algorithms
	SWOT VAC	No formal assessment is undertaken in SWOT VAC
	Examination period	LINK to Assessment Policy: http://policy.monash.edu.au/policy-bank/ academic/education/assessment/ assessment-in-coursework-policy.html

*Unit Schedule details will be maintained and communicated to you via your learning system.

Assessment Summary

Examination (3 hours): 60%; In-semester assessment: 40%

Assessment Task	Value	Due Date
Assignment 1	10%	Week 4
Test	15%	Week 6
Assignment 2	15%	Week 10
Examination 1	60%	To be advised

Teaching Approach

Lecture and tutorials or problem classes

Assessment Requirements

Assessment Policy

Faculty Policy - Unit Assessment Hurdles (http://www.infotech.monash.edu.au/resources/staff/edgov/policies/assessment-examinations/unit-assessment-hurdles.html)

Academic Integrity - Please see the Demystifying Citing and Referencing tutorial at http://lib.monash.edu/tutorials/citing/

Assessment Tasks

Participation

• Assessment task 1

  Title:

  Assignment 1

  Description:

  This assignment will aim to help you understand how to go about finding algorithms to solve problems.

  Weighting:

  10%

  Criteria for assessment:

  Detailed assessment criteria will be issued along with the assignment.   

  1. All assumptions should be stated.
  2. All algorithms must meet the problem specification.
  3. Students should be able to answer questions about their own work.

  Due date:

  Week 4

• Assessment task 2

  Title:

  Test

  Description:

  This test will be based on the first 4 weeks of material.

  Weighting:

  15%

  Criteria for assessment:

  Detailed assessment criteria will be issued along with the assignment.   

  1. All assumptions should be stated.
  2. All algorithms must meet the problem specification.
  3. Students should be able to answer questions about their own work.

  Due date:

  Week 6

• Assessment task 3

  Title:

  Assignment 2

  Description:

  This assignment will help you understand different search techniques.  It will also help you to communicate and reason about algorithms.

  Weighting:

  15%

  Criteria for assessment:

  Detailed assessment criteria will be issued along with the assignment.   

  1. All assumptions should be stated.
  2. All algorithms must meet the problem specification.
  3. Students should be able to answer questions about their own work.

  Due date:

  Week 10

Examinations

• Examination 1

  Weighting:

  60%

  Length:

  3 hours

  Type (open/closed book):

  Closed book

  Electronic devices allowed in the exam:

  None

Learning resources

Reading list

1. Levitin, A., Introduction to the Design and Analysis of Algorithms (2nd Edition), Addison-Wesley, 2006
2. Harel, D. with Y. Feldman, Algorithmics: The Spirit of Computing (3rd Edition), Pearson Education Limited, 2004.
3. Michalewicz, Z. and M. Michalewicz, Puzzle-Based Learning: An introduction to critical thinking, mathematics, and problem solving, Hybrid Publishers, 2008.
4. Polya, G., How to solve it; a new aspect of mathematical method (2nd Edition), Garden City, N.Y., Doubleday, 1957
5. Bentley, J., Programming Pearls, Addison-Wesley, 1986
6. Bentley, J., More Programming Pearls: confessions of a coder, Addison-Wesley,  1988
7. Skiena, S., The Algorithm Design Manual, TELOS--the Electronic Library of Science, 1998
8. Cormen, T., C.E. Leiserson, R.L. Rivest, and C. Stein, Introduction to Algorithms, The MIT Press, 1990

Feedback to you

• Informal feedback on progress in labs/tutes
• Graded assignments with comments
• Test results and feedback

Extensions and penalties

Returning assignments

Assignment submission

Online submission

If Electronic Submission has been approved for your unit, please submit your work via the learning system for this unit, which you can access via links in the my.monash portal.

Recommended text(s)

Levitin, A. (2006). Introduction to the Design and Analysis of Algorithms. (2nd Edition) Addison-Wesley.

Harel, D. with Feldman, Y. (2004). Algorithmics: The Spirit of Computing. (3rd Edition) Pearson Education Limited.

Other Information

Policies

Graduate Attributes Policy

Student services

Monash University Library

Disability Liaison Unit

Your feedback to Us

Previous Student Evaluations of this Unit

This unit was first offered in 2010 and in Semester 1, 2012, some of those students were taking third year units.  Evidence gathered over the years indicates that the introduction of FIT1029 has helped to improve the students' programming skills and improved the pass rates in core BCS and BSE units.

Feedback from students who have taken FIT1029 and have gone on to later year studies in the BCS and the BSE indicates that students feel that FIT1029 has helped them gain a better appreciation of algorithms, and to regard thinking about algorithms as an activity that should be kept separated from coding algorithms into a particular computer programming language.