Linear Search - Python

Given an array, arr of n elements, and an element x, find whether element x is present in the array. Return the index of the first occurrence of x in the array, or -1 if it doesn’t exist.

Examples:

Input: arr[] = [10, 50, 30, 70, 80, 20, 90, 40], x = 30
Output : 2
Explanation: For array [10, 50, 30, 70, 80, 20, 90, 40], the element to be searched is 30 and it is at index 2. So, the output is 2.

Explanation: A simple approach is to do a linear search, i.e

• Start from the leftmost element of arr[] and one by one compare x with each element of arr[].
• If x matches with an element, return the index.
• If x doesn't match with any of the elements, return -1.

Using Iterative Approach

This takes an array arr and a target element to search. Iterates through each element of the array and compares it with the target. If a match is found, it returns the index of the element. If the target is found, it returns its index; otherwise, it returns -1.

def linear_search(arr, target):
    for index in range(len(arr)):
        if arr[index] == target:
            return index
    return -1

# Example
arr = [10, 23, 45, 70, 11, 15]
target = 70

result = linear_search(arr, target)

if result != -1:
    print(f"Element found at index: {result}")
else:
    print("Element not found in the array")

Element found at index: 3

Using Recursive Approach

• Base Case: If the index reaches the end of the list (index == len(arr)), the function returns -1, indicating that the target element is not found in the list.
• Check Current Index: If the element at the current index matches the target, the function returns the index, indicating the position where the target was found.
• Recursive Case: If the current element is not the target, the function recursively calls itself with the next index (index + 1) to continue the search.
• Example Usage: The program searches for the target 30 in the list [10, 20, 30, 40, 50]. The function will find 30 at index 2 and return it.

def linear_search_recursive(arr, target, index=0):
    if index == len(arr):
        return -1 
        if arr[index] == target:
            return index
    
    # Recursive call to check the next element
    return linear_search_recursive(arr, target, index + 1)

# Example
arr = [10, 20, 30, 40, 50]
target = 30

result = linear_search_recursive(arr, target)

if result != -1:
    print(f"Element {target} found at index {result}.")
else:
    print(f"Element {target} not found in the list.")

Element 30 not found in the list.

Time complexity: O(n). 

Auxiliary Space: O(1) for iterative and O(n) for recursive.

Please refer complete article on Linear Search and Difference Between Recursive and Iterative Algorithms for more details!

Using RegEx Method

This function takes a list lst and a pattern as input

• re.compile(pattern): Compiles the given pattern into a regex object.
• Iterate Over the List: The program iterates over each element in the list.
• regex.search(element): Checks if the pattern exists within the current element.
• Return Result: If a match is found, it returns the element and its index; otherwise, it returns "Pattern not found in the list."

import re

def linear_search(lst, pattern):
    # Compile the pattern using re
    regex = re.compile(pattern)
    
    for index, element in enumerate(lst):
        if regex.search(element):
            return f"Pattern found in element '{element}' at index {index}"
    
    return "Pattern not found in the list"

# Example list
my_list = ["apple", "banana", "cherry", "date", "elderberry"]
pattern = "an" 
result = linear_search(my_list, pattern)
print(result)

Pattern found in element 'banana' at index 1

Time Complexity: The time complexity of this program is O(n) because we need to traverse the entire list to convert it to a string and count the number of commas before the match. The search using regular expression can also take some time depending on the length of the string, but it is generally considered to be a fast operation.

Space Complexity: The space complexity of this program is O(n) because we need to store the entire list as a string in memory. Additionally, the regular expression module uses some memory for its operations. However, since the input size is relatively small in this case, the space complexity is not a major concern.

kartik

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