programming-for-dummies

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Using Bubble Sort
Speed measures how fast the algorithm can sort data of different sizes. Some
sorting algorithms work quickly with small lists but slow down dramatically
when dealing with larger lists. Other sorting algorithms work quickly when a
list is mostly sorted but slow to a crawl when working with completely
unsorted lists.
Memory requirements define how much memory the sorting algorithm needs
to run. Some sorting algorithms can accept and sort data while they receive
the data, which is an online algorithm. (An offline algorithm has to wait to
receive the complete set of data before it can even start the sorting
process.) Sorting algorithms that use recursion (see Book II, Chapter 6) may
be easy to implement and fast but can require lots of memory to sort large
amounts of data.
Another factor that determines a sorting algorithm’s memory requirements is
whether the algorithm is an in-place algorithm. An in-place algorithm can sort
data by using the existing data structure that holds the data. For example, if
you have an array of numbers, an in-place algorithm can sort the data in that
array without needing a separate data structure to hold the data temporarily.
Stability refers to whether a sorting algorithm preserves the order of identical
data. Suppose you had a list of first and last names, such as John Smith
and Mary Smith, and the name John Smith appears at the beginning of a list
and Mary Smith appears at the end. A stable sorting algorithm sorts both
names and keeps John Smith ahead of Mary Smith, but an unstable algorithm
moves Mary Smith ahead of John Smith. (Out of all the sorting algorithms
presented in this chapter, heap sort is the only unstable algorithm.)
Ultimately, there’s no perfect sorting algorithm; however, a trade-off in size,
speed, and ease of implementation finds the best sorting algorithm.
Using Bubble Sort
The simplest way to sort any amount of data is to start at the beginning and
compare the first two adjacent items. So if you need to sort a list of numbers,
you compare the first number with the second number. If the first
number is bigger, you swap its place with the second number. If the second
number is bigger, you don’t do anything at all.
After comparing the first two items, you move down to comparing the
second and third items, and so on. Repetitively comparing two adjacent
items is the basic idea behind the bubble sort algorithm, as shown in
Figure 1-1. The bubble sort algorithm gets its name because small values
tend to bubble up to the top.