• 26 Jul 13:00

# COMPGC05 - Algorithmics

This database contains the 2016-17 versions of syllabuses. Syllabuses from the 2015-16 session are available here.

Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).

Code COMPGC05 MSc This course should be taken in conjunction with the core courses for MSc Computer Science and MSc Financial Computing. 2 Denise Gorse (50%), Jens Krinke (50%) To introduce more formal aspects of algorithms and data structures than in the first term. Properties of data types such as queues and search trees. Techniques for analysing the complexity and decidability of algorithms. Formal models of computation. Knowledge of a selection of standard data structures and abstract data types. The ability to choose appropriate data structures for programming problems. Understanding of a variety of common algorithms and why some are more efficient than others. Ability to discuss uncomputable and intractable algorithms.

# Content:

Course Introduction
Introduction to data structures and abstract data types; Abstract data types and Java classes; Language facilities; Model of storage in imperative languages; Cells, l-values and r-values; Exceptions; Data aggregation: arrays, structures and object references.

Linear abstract data types
Stacks and queues; Implementations with arrays and linked lists; Applications;  Sequences; Circular and double linked lists; Inheritance relationships between linear linked lists.

Tables
Linear look-up table; Linear search; Binary search.

Trees
Definitions of trees; Binary search tree; Insertion, deletion, tree traversal and searching.

Hash tables
Chaining; Open addressing; Choice of hash function.

Graphs
Adjacency lists; Adjacency matrices; Depth and breadth first traversal.

Sorting
Insertion Sort; Mergesort; Quicksort.

Analysis of Algorithms
Empirical vs theoretical analysis; Algorithmic complexity; O notation; Best, worst, and average cases; Sums of series; Simple summation formulae.

Analysis of earlier examples
Includes linear and binary search.

Limits of Computation
Tractable and intractable problems; the complexity classes P, NP, NPC Undecidability; the Halting Problem.

# Method of Instruction:

Lecture presentations, coursework and associated class problems.

# Assessment:

The course has the following assessment components

• Written Examination (2.5 hours, 85%)
• Coursework Section (15%, 5 pieces for 2% each, 1 piece for 5%)

To pass this course, students must:

• Obtain an overall pass mark of 50% for all sections combined.

# Resources:

Algorithmics - The Science of Computing. D.Harel, Addison-Wesley, 1989.

Clifford A. Shaffer, "Data Structures and Algorithm Analysisâ€ť,  Edition 3.2 (Java Version) <http://people.cs.vt.edu/shaffer/Book/>