Detecting a loop in a linked list is a common problem in computer science, and one way to solve it is by using Floyd’s algorithm, also known as the “tortoise and hare” algorithm. Floyd’s algorithm uses two pointers to traverse the linked list and detect if there is a loop in it.

Here’s how you can implement Floyd’s algorithm to detect a loop in a linked list:

``````function detectLoop(head):
// Initialize two pointers

// Traverse the linked list using two pointers
while fast != null and fast.next != null:
// Move slow pointer one step
slow = slow.next
// Move fast pointer two steps
fast = fast.next.next

// Check if slow and fast pointers meet
if slow == fast:
return true

// If the fast pointer reaches the end of the list, there is no loop
return false``````

In this pseudocode, `head` is the head of the linked list. We initialize two pointers called `slow` and `fast` to the head of the linked list. The `slow` pointer moves one step at a time, while the `fast` pointer moves two steps at a time.

If there is no loop in the linked list, the `fast` pointer will reach the end of the linked list before the `slow` pointer, and we can return `false` to indicate that there is no loop.

If there is a loop in the linked list, the `fast` pointer will eventually catch up to the `slow` pointer, meaning that they will both point to the same node in the loop. In this case, we can return `true` to indicate that there is a loop.

Note that Floyd’s algorithm has a time complexity of O(n), where n is the length of the linked list, and a space complexity of O(1), meaning it does not require any additional space to detect a loop in the linked list.

Code in Python:

``````class Node:
def __init__(self, data):
self.data = data
self.next = None

# initialize two pointers

while fast is not None and fast.next is not None:
# move slow pointer one step
slow = slow.next
# move fast pointer two steps
fast = fast.next.next

# check if slow and fast pointers meet
if slow == fast:
return True

return False
``````

Code in Ruby:

``````class Node
attr_accessor :data, :next

def initialize(data)
@data = data
@next = nil
end
end

while fast && fast.next
slow = slow.next
fast = fast.next.next

if slow == fast
return true
end
end

return false
end
``````

Code in Java:

``````class Node {
int data;
Node next;

Node(int data) {
this.data = data;
this.next = null;
}
}

while (fast != null && fast.next != null) {
slow = slow.next;
fast = fast.next.next;

if (slow == fast) {
return true;
}
}

return false;
}
``````

Code in JavaScript:

``````class Node {
constructor(data) {
this.data = data;
this.next = null;
}
}

while (fast && fast.next) {
slow = slow.next;
fast = fast.next.next;

if (slow === fast) {
return true;
}
}

return false;
}``````

In all of these implementations, we first define a `Node` class to represent each node in the linked list. Then, we define a function called `detect_loop` or `detectLoop` that takes the head of the linked list as a parameter.

The function initializes two pointers called `slow` and `fast` to the head of the linked list, and then uses a while loop to traverse the linked list. The `slow` pointer moves one step at a time, while the `fast` pointer moves two steps at a time.

If there is no loop in the linked list, the `fast` pointer will reach the end of the linked list before the `slow` pointer, and we can return `false` to indicate that there is no loop.
If there is a loop in the linked list, the `fast` pointer will eventually catch up to the `slow` pointer, meaning that they will both point to the same node in the loop. In this case, we can return `true` to indicate that there is a loop.