# Design and Analysis of Algorithms:  2019-2020

 Lecturer Daniel Kroening Degrees Term Hilary Term 2020  (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.

The lectures for this course are recorded, and the recordings released at the end of each week of term. Please click here to access the recordings.

## 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 worst-case and average-case 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. 
• Algorithm design strategies: top down design, divide and conquer. Application to sorting and searching and to matrix algorithms. Solution of relevant recurrence relations. 
• Data structures and their representations: arrays, lists, stacks, queues, trees, heaps, priority queues, graphs. 
• Introduction to discrete optimisation algorithms:  dynamic programming, greedy algorithms, shortest path problems. 
• Graph algorithms: examples of depth-first and breadth-first search algorithms. Topological sorting, connected components. 

## Syllabus

Basic strategies of algorithm design: top-down design, divide and conquer, average and worst-case 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, shortest-path and spanning tree problems. Introduction to discrete optimisation algorithms: dynamic programming, greedy algorithms. Graph algorithms: depth first and breadth first search.