# Data Structure Assignment

**Topics:**Sorting algorithm, Quicksort, Comparison sort

**Pages:**21 (4612 words)

**Published:**April 13, 2011

Types of searching

2.1 Binary search tree

In computer science, a binary search tree (BST) is a node based binary tree data structure which has the following properties: * The left subtree of a node contains only nodes with keys less than the node's key. * The right subtree of a node contains only nodes with keys greater than the node's key. * Both the left and right subtrees must also be binary search trees.

From the above properties it naturally follows that:

* Each node (item in the tree) has a distinct key.

Generally, the information represented by each node is a record rather than a single data element. However, for sequencing purposes, nodes are compared according to their keys rather than any part of their their associated records. The major advantage of binary search trees over other data structures is that the related sorting algorithms and search algorithms such as in-order traversal can be very efficient. Binary search trees are a fundamental data structure used to construct more abstract data structures such as sets, multisets, and associative arrays.

A binary search tree of size 9 and depth 3, with root 8 and leaves 1, 4, 7 and 13 -------------------------------------------------

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1.2 Linear and sequential search

In computer science, linear search or sequential search is a method for finding a particular value in a list, that consists of checking every one of its elements, one at a time and in sequence, until the desired one is found. Linear search is the simplest search algorithm; it is a special case of brute-force search. Its worst case cost is proportional to the number of elements in the list; and so is its expected cost, if all list elements are equally likely to be searched for. Therefore, if the list has more than a few elements, other methods (such as binary search or hashing) will be faster, but they also impose additional requirements. -------------------------------------------------

1.2.1 Analysis

For a list with n items, the best case is when the value is equal to the first element of the list, in which case only one comparison is needed. The worst case is when the value is not in the list (or occurs only once at the end of the list), in which case n comparisons are needed. If the value being sought occurs k times in the list, and all orderings of the list are equally likely, the expected number of comparisons is

For example, if the value being sought occurs once in the list, and all orderings of the list are equally likely, the expected number of comparisons is . However, if it is known that it occurs once, then at most n - 1 comparisons are needed, and the expected number of comparisons is

(for example, for n = 2 this is 1, corresponding to a single if-then-else construct). Either way, asymptotically the worst-case cost and the expected cost of linear search are both O(n).

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1.2.2 Pseudocode

Forward iteration

The following pseudocode describes a typical variant of linear search, where the result of the search is supposed to be either the location of the list item where the desired value was found; or an invalid location Λ, to indicate that the desired element does not occur in the list. -------------------------------------------------

For each item in the list:

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if that item has the desired value,

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stop the search and return the item's location.

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Return Λ.

In this pseudocode, the last line is executed only after all list items have been examined with none matching. If the list is stored as an array data structure, the location may be the index of the item found (usually between 1 and n, or 0 and n−1). In that case the invalid...

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