# Introduction

Input: A sequence of n numbers <a1, a2, . . . , an>.

Output: A permutation (reordering) <a'1, a'2, . . . , a'n> of the input sequence such that a'1 a'2 a'n.

The input sequence is usually an n-element array, although it may be represented in some other fashion, such as a linked list.

## The structure of the data

In a sense, it is these implementation details that distinguish an algorithm from a full-blown program. Whether we sort individual numbers or large records that contain numbers is irrelevant to the method by which a sorting procedure determines the sorted order. Thus, when focusing on the problem of sorting, we typically assume that the input consists only of numbers. The translation of an algorithm for sorting numbers into a program for sorting records is conceptually straightforward, although in a given engineering situation there may be other subtleties that make the actual programming task a challenge.

## Sorting algorithms

In this part, we shall introduce two more algorithms that sort arbitrary real numbers. Heapsort, presented in Chapter 7, sorts n numbers in place in O(n lg n) time. It uses an important data structure, called a heap, to implement a priority queue.

Quicksort, in Chapter 8, also sorts n numbers in place, but its worst-case running time is (n2). Its average-case running time is (n lg n), though, and it generally outperforms heapsort in practice. Like insertion sort, quicksort has tight code, so the hidden constant factor in its running time is small. It is a popular algorithm for sorting large input arrays.

Insertion sort, merge sort, heapsort, and quicksort are all comparison sorts: they determine the sorted order of an input array by comparing elements. Chapter 9 begins by introducing the decision-tree model in order to study the performance limitations of comparison sorts. Using this model, we prove a lower bound of (n lg n) on the worst-case running time of any comparison sort on n inputs, thus showing that heapsort and merge sort are asymptotically optimal comparison sorts.

Chapter 9 then goes on to show that we can beat this lower bound of (n lg n) if we can gather information about the sorted order of the input by means other than comparing elements. The counting sort algorithm, for example, assumes that the input numbers are in the set {l, 2, . . . , k}. By using array indexing as a tool for determining relative order, counting sort can sort n numbers in O(k + n) time. Thus, when k = O(n), counting sort runs in time that is linear in the size of the input array. A related algorithm, radix sort, can be used to extend the range of counting sort. If there are n integers to sort, each integer has d digits, and each digit is in the set {1,2, . . . , k}, radix sort can sort the numbers in O(d (n + k)) time. When d is a constant and k is O(n), radix sort runs in linear time. A third algorithm, bucket sort, requires knowledge of the probabilistic distribution of numbers in the input array. It can sort n real numbers uniformly distributed in the half-open interval [0, 1) in average-case O(n) time.

## Order statistics

The ith order statistic of a set of n numbers is the ith smallest number in the set. One can, of course, select the ith order statistic by sorting the input and indexing the ith element of the output. With no assumptions about the input distribution, this method runs in (n lg n) time, as the lower bound proved in Chapter 9 shows.

In Chapter 10, we show that we can find the ith smallest element in O(n) time, even when the elements are arbitrary real numbers. We present an algorithm with tight pseudocode that runs in O(n2) time in the worst case, but linear time on average. We also give a more complicated algorithm that runs in O(n) worst-case time.

## Background

Although most of this part does not rely on difficult mathematics, some sections do require mathematical sophistication. In particular, the average-case analyses of quicksort, bucket sort, and the order-statistic algorithm use probability, which is reviewed in Chapter 6. The analysis of the worst-case linear-time algorithm for the order statistic involves somewhat more sophisticated mathematics than the other worst-case analyses in this part.