This unit introduces students to problem solving concepts and techniques fundamental to the science of programming. In doing this it covers problem specification, algorithmic design, analysis and implementation. Detailed topics include analysis of best, average and worst-case time and space complexity; introduction to numerical algorithms; recursion; advanced data structures such as heaps and B-trees; hashing; sorting algorithms; searching algorithms; graph algorithms; and numerical computing.
At the completion of this unit students will have:
- understanding of a formal specification;
- ability to create a formal specification for an informal problem;
- knowledge and understanding of algorithmic properties such as correctness, termination and complexity;
- ability to, given a non-trivial algorithm, formally prove certain properties, such as correctness and termination;
- ability, given a non-trivial algorithm, to determine its best, average and worst-case, time and space-complexity;
- knowledge and understanding of reasonably complex data structures such as minimum spanning trees, and Directed and Undirected, Weighted and Unweighted Graphs;
- ability to design and implement new non-trivial algorithms using complex data structures;
- knowledge of and ability to use algorithmic paradigms such as divide and conquer, greedy, dynamic programming and so on;
- ability to identify these paradigms in diverse algorithms;
- knowledge and understanding of the issues involved in implementing a non-trivial algorithm efficiently.
Developed attitudes that enable them to:
- carefully design and/or analyse the algorithms they are using in order to verify important properties such as correctness, termination, and complexity;
- identify the key features of a brief informal problem description and abstract the underlying formal problem.
Developed the skills to:
- create their own data structures.
- create a new algorithm to solve a new problem.
- make a formal argument about desirable properties of the solution.
- adapt an existing algorithm and/or data-structure where that is possible and appropriate.
- implement a non-trivial algorithm efficiently.
Demonstrated the communication skills necessary to:
- make a formal argument that an algorithm and/or data-structure has a given property, such as correctness, termination or complexity.
Examination (3 hours): 70%; In-semester assessment: 30%
Minimum total expected workload equals 12 hours per week comprising:
(a.) Contact hours for on-campus students:
- Two hours of lectures
- One 3-hour laboratory per fortnight (alternating with the tutorial)
- One 1-hour tutorial per fortnight (alternating with the lab)
(b.) Additional requirements (all students):
- A minimum of 8 hours of independent study per week for lab preparation and reading.
One of FIT1008, FIT1015 or CSE1303 and 6 points of Level 1 mathematics.