In this tutorial you will learn about the Difference Between Linear Search vs Binary Search and its application with practical example.
What Is Linear Search?
In this searching process comparison of data element is done with elements present in the list step by step, this type of searching is also called as sequential searching. The process in linear search starts with the collation of element already available in list’s first element and if both the elements seems same then it returns the index value or else it returns -1 and then this process continues with the other element present in the list and if the element doesn’t found in the list then this searching becomes unsuccessful. The average case complexity of linear search is O (n).
What Is Binary Search?
In this searching method any kind of structural big data can easily be searched and sorted in binary search, this technique is very fast searching algorithm whose time complexity is O (log n), this technique is based on the principle of divide and conquer. Binary search is executed in only that kind of list which is sorted sequentially. In this searching element process it is compared with the middle element, if both elements are same then it returns index value and if not then it is checked with the middle element in big or small manner. If element is big then it is repeated in middle part and it is executed up to the completion of finding of element.
Linear Search vs Binary Search
|LINEAR SEARCH||BINARY SEARCH|
|Comparison of element done with the data already present in the list||Comparison of element done in the middle part of the element present in the list|
|Element are not arranged in a sorted manner||Elements have to be arranged in the sorted manner|
|Sequential search is needed in this technique||Divide and conquer process is needed in this technique|
|It is implemented in linear data structure like array ,linked list etc.||It is implemented on the data who have two way transversal|
|It usually denoted small data size||It denotes the large and big data size|
|Finding the element O (n) is its worst case||Finding the element O (log2n) is its worst case|