Introduction to Algorithmics
This course introduces students to the notion of efficiency and computational complexity. The basic data structures encountered in first year, such as lists, trees and graphs, are reviewed in light of their efficiency and common usage scenario. Asymptotic measures of complexity are covered, and recurrence relations are introduced as an analytical tool. Problem-solving techniques such as the greedy strategy, divide-and-conquer, dynamic programming, and graph searching are covered. These techniques are illustrated upon optimization problems chosen for their practical relevance.
|Faculty||Faculty of Engineering and Built Environment|
|School||School of Electrical Engineering and Computer Science|
Semester 2 - 2014
Semester 2 - 2015 (Callaghan Campus)
(1) To introduce students to efficient algorithm design techniques.
(2) To introduce students to basic techniques regarding analysis of performance of algorithms.
(3) To make students familiar with the most important basic algorithms used in various computer science application and theoretical areas.
(1) Preliminaries (review of basic mathematical notions, data structures, induction, basic combinatorics).
(2) Elementary algorithmics (worst-case vs. average case, basic examples, elementary operations).
(3) Asymptotic Notation (big O, Omega and Theta).
(4) Analysis of Algorithms (loops, recurrence relations).
(5) Data structures (graphs, trees, heaps, disjoint sets).
(6) Searching and Sorting.
(7) Greedy algorithms.
(9) Dynamic programming.
(10) Text-search Algorithm.
(11) Introduction to the topics of computational complexity, heuristics, metaheuristics and approximation algorithms.
|Assumed Knowledge||SENG6120 Knowledge of discrete mathematics.|
|Modes of Delivery||Internal Mode
Problem Based Learning
|Course Materials||None listed|
|Timetable||2015 Course Timetables for COMP6230|