Design and Analysis of Algorithms: 20172018
Lecturer 

Degrees 
Preliminary Examinations — Computer Science and Philosophy 
Term 
Hilary Term 2018 (16 lectures) 
Overview
This core course covers good principles of algorithm design, elementary analysis of algorithms, and fundamental data structures. The emphasis is on choosing appropriate data structures and designing correct and efficient algorithms to operate on these data structures.Learning outcomes
This is a first course in data structures and algorithm design. Students will:
 learn good principles of algorithm design;
 learn how to analyse algorithms and estimate their worstcase and averagecase behaviour (in easy cases);
 become familiar with fundamental data structures and with the manner in which these data structures can best be implemented; become accustomed to the description of algorithms in both functional and procedural styles;
 learn how to apply their theoretical knowledge in practice (via the practical component of the course).
Synopsis
 Program costs: time and space. Worst case and average case analysis. Asymptotics and "big O" notation. Polynomial and exponential growth. Asymptotic estimates of costs for simple algorithms. Use of induction and generating functions. [2]
 Algorithm design strategies: top down design, divide and conquer. Application to sorting and searching and to matrix algorithms. Solution of relevant recurrence relations. [4]
 Data structures and their representations: arrays, lists, stacks, queues, trees, heaps, priority queues, graphs. [3]
 Introduction to discrete optimisation algorithms: dynamic programming, greedy algorithms, shortest path problems. [3]
 Graph algorithms: examples of depthfirst and breadthfirst search algorithms. Topological sorting, connected components. [3]
Syllabus
Basic strategies of algorithm design: topdown design, divide and conquer, average and worstcase criteria, asymptotic costs. Simple recurrence relations for asymptotic costs. Choice of appropriate data structures: arrays, lists, stacks, queues, trees, heaps, priority queues, graphs. Applications to sorting and searching, matrix algorithms, shortestpath and spanning tree problems. Introduction to discrete optimisation algorithms: dynamic programming, greedy algorithms. Graph algorithms: depth first and breadth first search.
Reading list
 T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein. Introduction to Algorithms, 3rd edition, MIT Press, 2009 (2nd edition [2001] or 1st edition, [1990] can be used as well). This is the main text book for this lecture course.
 M. T. Goodrich and R. Tommassia. Algorithm Design, Wiley, 2002.
 S. Dasgupta, C. Papadimitriou, and U. Vazirani. Algorithms. McGrawHill Higher Education. 2006