# Binary Search

Binary search is an efficient algorithm for finding a specific element in a sorted array or list. The key principle behind binary search is to divide the search interval in half with each step, significantly reducing the time it takes to find the target element compared to linear search, especially in large datasets. Here's a step-by-step explanation using an example with 10 elements:

**Array**: First, we need a sorted array. Let's say our array is`[1, 3, 4, 7, 9, 11, 12, 14, 17, 19]`

, and we want to search for the number`11`

.**Initial Variables**: We start with two pointers, usually named`low`

and`high`

. Initially,`low`

is set to the first index (0) and`high`

is set to the last index (9 in this case).**Middle Element**: We find the middle element of the array. The middle index,`mid`

, is calculated as`(low + high) / 2`

. In our case, it's`(0 + 9) / 2 = 4.5`

, which we round down to`4`

. The middle element is the one at index 4, which is`9`

.**Comparison**: We compare the middle element with the target value (`11`

). Since`9`

is less than`11`

, we discard the left half of the array including the middle element. Our new search space is`[11, 12, 14, 17, 19]`

.**Update Pointers**: We update`low`

to be`mid + 1`

(which is now 5) and keep`high`

as it is (9).**Repeat Process**: We repeat the process - find the middle of the new range, compare it with the target, and decide which half to keep. The new middle element is at index`(5 + 9) / 2 = 7`

, which is`14`

.**Comparison Again**: Since`14`

is greater than`11`

, we discard the right half of the current array. Our search space is now reduced to just`[11]`

.**Final Step**: We repeat the process. The middle of our new range`[11]`

is`11`

, which matches our target.**Conclusion**: Since we've found the target, we conclude the search. If at any point our`low`

exceeds`high`

, it means the element isn't in the array.

Let's take another example of an array with 20 elements. Consider the sorted array:

`[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40]`

Suppose we want to search for the number `26`

.

```
Initial Array:
[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40]
^ ^ ^
low mid high
Step 1: Since 26 > 20 (element at mid), discard left half including mid
[22, 24, 26, 28, 30, 32, 34, 36, 38, 40]
^ ^ ^
low mid high
Step 2: Since 26 < 30 (element at mid), discard right half including mid
[22, 24, 26, 28]
^ ^ ^
low mid high
Step 3: Since 26 > 24 (element at mid), discard left half including mid
[26, 28]
^
mid
Finally, we find 26 at mid.
```

Binary search halves the search space at each step, significantly reducing the number of comparisons needed to find the target or determine its absence. This makes binary search very efficient, especially for large datasets.

Let's write the pseudo-code for binary search and explain each step. The algorithm is applied on a sorted array.

### Pseudo Code

```
function binarySearch(array, target)
low = 0
high = length(array) - 1
while low <= high
mid = (low + high) / 2
if array[mid] == target
return mid // Target found
if array[mid] < target
low = mid + 1 // Search in the right half
else
high = mid - 1 // Search in the left half
return -1 // Target not found
```

### Explanation

**Initialize Variables**:`low`

and`high`

are pointers to track the current search interval. Initially,`low`

is set to the start of the array (index 0), and`high`

is set to the end of the array (index`length(array) - 1`

).

**Binary Search Loop**:- The
`while`

loop continues as long as`low`

is less than or equal to`high`

, meaning there's still a range to search.

- The
**Calculate Midpoint**:- The midpoint
`mid`

is calculated as the average of`low`

and`high`

. This is the index we check against the target.

- The midpoint
**Check Midpoint**:If

`array[mid]`

is equal to the target, the function returns`mid`

. This means the target has been found.If

`array[mid]`

is less than the target, the target must be in the right half of the array. So, we set`low`

to`mid + 1`

.If

`array[mid]`

is greater than the target, the target must be in the left half. So, we set`high`

to`mid - 1`

.

**Target Not Found**:- If the loop exits without returning, it means the target is not in the array. We return
`-1`

or some other indicator of failure.

- If the loop exits without returning, it means the target is not in the array. We return

This algorithm is efficient for large datasets because it halves the search space with each step, having a time complexity of O(log n), where n is the number of elements in the array. However, it's crucial that the array is sorted before applying binary search.

Below is an example of a binary search algorithm implemented in C.

### C Code for Binary Search

```
#include <stdio.h>
int binarySearch(int arr[], int size, int target) {
int low = 0;
int high = size - 1;
while (low <= high) {
int mid = low + (high - low) / 2;
if (arr[mid] == target) {
return mid; // Target found
}
if (arr[mid] < target) {
low = mid + 1; // Search in the right half
} else {
high = mid - 1; // Search in the left half
}
}
return -1; // Target not found
}
int main() {
int arr[] = {2, 3, 4, 10, 14, 23, 31, 45, 56, 65};
int size = sizeof(arr) / sizeof(arr[0]);
int target = 23;
int result = binarySearch(arr, size, target);
if (result != -1) {
printf("Element is found at index %d\n", result);
} else {
printf("Element not found in the array\n");
}
return 0;
}
```

### Explanation

**Include Directive**:`#include <stdio.h>`

is used to include the Standard Input Output header file, which is required for using the`printf`

function.**The**`binarySearch`

Function:It takes three parameters: the array

`arr`

, its`size`

, and the`target`

element to search for.Variables

`low`

and`high`

are initialized to mark the beginning and end of the array.A

`while`

loop runs as long as`low`

is less than or equal to`high`

.Inside the loop,

`mid`

is calculated as the average of`low`

and`high`

. To avoid potential overflow, it's calculated as`low + (high - low) / 2`

instead of`(low + high) / 2`

.If

`arr[mid]`

is equal to the target, the function returns the index`mid`

.If

`arr[mid]`

is less than the target, the search continues in the right half by setting`low`

to`mid + 1`

.If

`arr[mid]`

is greater than the target, the search continues in the left half by setting`high`

to`mid - 1`

.If the target is not found, the function returns

`-1`

.

**The**`main`

Function:Defines an array

`arr`

and its size`size`

.Specifies the

`target`

element to search for.Calls the

`binarySearch`

function and stores the result in`result`

.Prints the index where the element is found or a message if it's not found.

**Return Statement**: The`main`

function returns`0`

, indicating successful program termination.

Complexity analysis of the binary search algorithm involves examining both its time complexity and space complexity:

### Time Complexity

**Best Case**: O(1)- The best-case scenario occurs when the target element is at the midpoint of the array during the first iteration. In this case, only one comparison is needed, and the algorithm terminates immediately.

**Average and Worst Case**: O(log n)In each step of binary search, the array is effectively halved. This means that the time to search the array grows logarithmically with the size of the array.

Mathematically, at most (\log_2 n + 1) comparisons are needed, where ( n ) is the number of elements in the array. This logarithmic growth makes binary search very efficient for large datasets.

### Space Complexity

**Iterative Approach (as in the provided C code)**: O(1)The iterative version of binary search, like the one you have in the C code, has a constant space complexity because it uses a fixed amount of space (for variables

`low`

,`high`

,`mid`

, etc.) regardless of the input size.There are no additional data structures used that grow with input size.

**Recursive Approach**: O(log n)- If binary search is implemented using recursion, the space complexity can become O(log n) due to the use of the call stack. Each recursive call adds a layer to the stack, and there can be up to (\log_2 n) calls in the worst case.

Binary search is highly efficient in terms of time complexity, especially compared to linear search, which has a time complexity of O(n). The space efficiency is also excellent in the iterative approach, making it a preferred method for searching in sorted arrays or lists.