Title:  Nonlinear Data Struct. & Alg. 
Long Title:  Nonlinear Data Struct. & Alg. 
Field of Study: 
Computer Science

Valid From: 
Semester 1  2017/18 ( September 2017 ) 
Module Coordinator: 
Sean McSweeney 
Module Author: 
Ignacio Castineiras 
Module Description: 
Data structures and algorithms are fundamental elements in many computing applications. In computer programs data structures offer different techniques for storing data while algorithms provide the methods for manipulating this data. In this module the learner will will be introduced to asymptotic cost analysis in order to assess the efficiency of algorithms and data structures when solving a computer sciencerelated problem. The module will examine and assess dynamic programming and backtracking algorithms using nonlinearbased abstract data types.

Learning Outcomes 
On successful completion of this module the learner will be able to: 
LO1 
Assess an algorithms computation complexity in terms of time and memory. 
LO2 
Compare and contrast the interfaces and internal representation of a number of nonlinear abstract data types. 
LO3 
Design and specify the operations of a nonlinearbased abstract data type in a declarative manner and implement them in a highlevel programming language. 
LO4 
Assess the applicability of dynamic programming and backtracking algorithms to realworld problems. 
LO5 
Design and implement dynamic programming and backtracking algorithms and compare their formulations and solutions. 
Prerequisite learning 
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named CIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s). 
12788 
COMP7035 
Linear Data Struct. & Alg. 
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list. 
No incompatible modules listed 
Corequisite Modules

No Corequisite modules listed 
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.

No requirements listed 
Module Content & Assessment
Indicative Content 
Algorithm Computational Complexity.
Evaluation of algorithm efficiency: Time and memory factors.
Formal asymptotic cost analysis: Big O notation. Best, worse and average cases.

Nonlinearbased Abstract Dataypes (ADTs).
Declarative semantics of nonlinearbased ADTs.
ADT binary tree specification and implementation: Minimal set of operations. Extending the interface: Traversal, path properties and other supplementary operations.
Comparable data typebased ADTs: Binary Search Trees (BST).

Graphbased ADTs.
Declarative semantics as a generalisation of treebased ADTs.
Graph main concepts (subgraph, path, cycle, connection) and categories (directed, weighted).

Dynamic Programming Algorithms.
Tail recursion. Decreasing recursive design vs. increasing iterative design.
Applications: Graphs, searching and resource allocation problems.

Backtracking Algorithms.
Satisfaction vs. Optimisation problems. Decision making: Explicit and implicit constraints. Exhaustive search: Alive, expansion and dead nodes. Prunning function.
Applications: Puzzles, graphs and resource allocation problems.

Assessment Breakdown  % 
Course Work  50.00% 
End of Module Formal Examination  50.00% 
Course Work 
Assessment Type 
Assessment Description 
Outcome addressed 
% of total 
Assessment Date 
Project 
Define, specify and document the set of operations for a novel ADT. Implement the set of operations of the ADT using an internal representation based nonlinear data structures. Produce a report to justify the data structures being chosen in terms of the time and memory complexities for the algorithms implementing the operations. 
1,2,3 
25.0 
Week 6 
Project 
Design, implement and document a dynamic programming or backtracking algorithm to tackle some reallife problems. Produce a report to justify the algorithm family being selected in terms of how effective it is to model the problem domain. 
1,4,5 
25.0 
Week 11 
End of Module Formal Examination 
Assessment Type 
Assessment Description 
Outcome addressed 
% of total 
Assessment Date 
Formal Exam 
End of Semester Formal Examination. 
1,2,3,4,5 
50.0 
EndofSemester 
Reassessment Requirement 
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

The institute reserves the right to alter the nature and timings of assessment
Module Workload
Workload: Full Time 
Workload Type 
Workload Description 
Hours 
Frequency 
Average Weekly Learner Workload 
Lecture 
Lecture deliverying theory underpinning learning outcomes. 
2.0 
Every Week 
2.00 
Lab 
Practical computerbased lab supporting learning outcomes. 
2.0 
Every Week 
2.00 
Independent & Directed Learning (Noncontact) 
Independent Study. 
3.0 
Every Week 
3.00 
Total Hours 
7.00 
Total Weekly Learner Workload 
7.00 
Total Weekly Contact Hours 
4.00 
Workload: Part Time 
Workload Type 
Workload Description 
Hours 
Frequency 
Average Weekly Learner Workload 
Lecture 
Lecture deliverying theory underpinning learning outcomes. 
2.0 
Every Week 
2.00 
Lab 
Practical computerbased lab supporting learning outcomes. 
2.0 
Every Week 
2.00 
Independent & Directed Learning (Noncontact) 
Independent Study. 
3.0 
Every Week 
3.00 
Total Hours 
7.00 
Total Weekly Learner Workload 
7.00 
Total Weekly Contact Hours 
4.00 
Module Resources
Recommended Book Resources 

 Thomas H. Cormen et. al. 2009, Introduction to Algorithms, 3rd Ed., MIT Press [ISBN: 9780262033848]
 Robert Sedgewick and Kevin Wayne 2011, Algorithms, 4th Ed., AddisonWesley [ISBN: 9780321573513]
 Supplementary Book Resources 

 Narashimha Karumanchi 2016, Data Structures And Algorithms Made Easy, CareerMonk [ISBN: 9788193245279]
 Christopher Steiner 2012, Automate This: How Algorithms Came to Rule Our World, Penguin [ISBN: 9781591844921]
 John V. Guttag 2013, Introduction to Computation and Programming Using Python, MIT Press [ISBN: 9780262525008]
 Michael T. Goodrich et. al. 2014, Data Structures and Algorithms in Java, Wiley Publishing [ISBN: 9781118771334]
 Michael T. Goodrich et. al. 2013, Data Structures and Algorithms in Python, Wiley Publishing [ISBN: 9781118290279]
 This module does not have any article/paper resources 

Other Resources 

 Website: Learn to think as a Computer Scientist
 Website: Data Structure Visualizations
 Website: CodinGame: Practice coding with fun
programming challenges
 Website: Python documentation
 Website: Java documentation

Module Delivered in
