The binary search algorithm is one of the most efficient searching techniques which require the list to be sorted in ascending order.

**BINARY SEARCH**

♦ The
binary search algorithm is one of the most efficient searching techniques which
require the list to be sorted in ascending order.

♦ To search
for an element in the list, the binary search algorithms split the list and
locate the middle element of the list.

♦ The
middle of the list is calculated as

Middle:=(l+r)
div 2

n –
number of element in list

♦ The
algorithm works by comparing a search key element ‗k‘ with the array middle
element a[m]

After
comparison, any one of the following three conditions occur.

1. If the
search key element 'k‘ is greater than a[m],then the search element is only in
the upper or second half and eliminate the element present in the lower
half.Now the value of l is middle m+1.

2. If the
search key element 'k‘ is less than a[m], then the search element is only in
the lower or first half. No need to check in the upper half. Now the value of r
is middle m - 1.

3. If the
search key element 'k‘ is equal to a[m], then the search key element is found
in the position m. Hence the search operation is complete.

**EXAMPLE:**

The list
of element are 3,14,27,31,39,42,55,70,74,81,85,93,98 and searching for k=70 in
the list.

m – middle element

m = n div 2

=13 div 2

m = 6

If
k>a[m], then ,l =7. So, the search element is present in second half .

Hence,
the search key element 70 is found in the position 7 and the search operation
is completed.

*ALGORITHM:
**Binary search (A[0..n –
1],k) //Implements nonrecursive
binary search*

// *Input:: An array A[0..n – 1] sorted in ascending
order and a search key k*

// *Output: An index of the array’s element that is
equal to k or -1 if there is no*

//
*such
element*

*l*^{!}* 0; r *^{!}* n – 1
while l<= r do*

*m *^{!}* └(l +
r)/ 2 ┘*

*if k = A[m] return m*

*else if k < A[m] r *^{!}* m – 1
else l *^{!}* m+ 1*

*return -1*

** EFFICIENCY OF BINARY SEARCH**

♦ The
standard way to analyze the efficiency is to count number of times search key
is compared with an element of the array.

**WORST CASE ANALYSIS**

♦ The worst
case include all array that do not contain a search key.

♦ The
recurrence relation for C_{worst}(n) is

= 1 +
└log_{2}i**┘** + 1 =2 +└ log_{2}i**┘**

**C _{worst}(n **

R.H.S
becomes

C_{worst}**└** n / 2 **┘**+ 1= C_{worst}└2i
/2**┘** + 1 =C_{worst}(i) + 1

= log_{2}i
+ 1 + 1

= 2 +└ log_{2}i**┘**

C_{worst}**└** n / 2**┘** + 1 =2 +└ log_{2}i**┘**

L.H.S
=R.H.S

Hence

C_{worst}(n
) = **└**log_{2}n **┘**+ 1 and

C_{worst}(i
) = **└**log_{2}i **┘**+ 1 are same

Hence

From equation (4) to search a element in a array of
1000 elements ,binary search takes. **└ **log_{2}10^{3}**
┘ **+ 1 = 10
key comparison

**AVERAGE CASE ANALYSIS:**

Average
number of key comparison made by the binary search is slightly smaller than
worst case.

Cavg (n)
≈ log_{2} n

The average number of comparison in the successful
search is C^{yes} _{avg} (n) ≈ log_{2} n – 1

The average number of comparison in the
unsuccessful search is C^{no} _{avg} (n) ≈ log_{2} (n +
1)

**ADVANTAGES**

1. In this
method elements are eliminated by half each time .So it is very faster than the
sequential search.

2. It
requires less number of comparisons than sequential search to locate the search
key element.

**DISADVANTAGES**

1. An
insertion and deletion of a record requires many records in the existing table
be physically moved in order to maintain the records in sequential order.

2. The ratio
between insertion/deletion and search time is very high.

Study Material, Lecturing Notes, Assignment, Reference, Wiki description explanation, brief detail

Analysis and Design of Algorithm : Divide and Conquer, Greedy Method : Binary Search Algorithm |

**Related Topics **

Privacy Policy, Terms and Conditions, DMCA Policy and Compliant

Copyright © 2018-2023 BrainKart.com; All Rights Reserved. Developed by Therithal info, Chennai.