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207. Write a code to find if two linked list intersect.
Microsoft Interview Questions and Answers
(Continued from previous question...)
207. Write a code to find if two linked list intersect.
Question:
Write a code to find if two linked list intersect.
maybe an answer:
Method 1(Simply use two loops)
Use 2 nested for loops. Outer loop will be for each node of the 1st list and inner loop will be for 2nd list. In the inner loop, check if any of nodes of 2nd list is same as the current node of first linked list. Time complexity of this method will be O(mn) where m and n are the number of nodes in two lists.
Method 2 (Mark Visited Nodes)
This solution requires modifications to basic linked list data structure. Have a visited flag with each node. Traverse the first linked list and keep marking visited nodes. Now traverse second linked list, If you see a visited node again then there is an intersection point, return the intersecting node. This solution works in O(m+n) but requires additional information with each node. A variation of this solution that doesn’t require modification to basic data structure can be implemented using hash. Traverse the first linked list and store the addresses of visited nodes in a hash. Now traverse the second linked list and if you see an address that already exists in hash then return the intersecting node.
Method 3(Using difference of node counts)
1) Get count of the nodes in first list, let count be c1.
2) Get count of the nodes in second list, let count be c2.
3) Get the difference of counts d = abs(c1 – c2)
4) Now traverse the bigger list from the first node till d nodes so that from here onwards both the lists have equal no of nodes.
5) Then we can traverse both the lists in parallel till we come across a common node. (Note that getting a common node is done by comparing the address of the nodes)
Time Complexity: O(m+n)
Auxiliary Space: O(1)
Method 4(Make circle in first list)
Thanks to Saravanan Man for providing below solution.
1. Traverse the first linked list(count the elements) and make a circular linked list. (Remember last node so that we can break the circle later on).
2. Now view the problem as find the loop in the second linked list. So the problem is solved.
3. Since we already know the length of the loop(size of first linked list) we can traverse those many number of nodes in second list, and then start another pointer from the beginning of second list. we have to traverse until they are equal, and that is the required intersection point.
4. remove the circle from the linked list.
Time Complexity: O(m+n)
Auxiliary Space: O(1)
Method 5 (Reverse the first list and make equations)
Thanks to Saravanan Mani for providing this method.
1) Let X be the length of the first linked list until intersection point.
Let Y be the length of the second linked list until the intersection point.
Let Z be the length of the linked list from intersection point to End of
the linked list including the intersection node.
We Have
X + Z = C1;
Y + Z = C2;
2) Reverse first linked list.
3) Traverse Second linked list. Let C3 be the length of second list - 1.
Now we have
X + Y = C3
We have 3 linear equations. By solving them, we get
X = (C1 + C3 – C2)/2;
Y = (C2 + C3 – C1)/2;
Z = (C1 + C2 – C3)/2;
WE GOT THE INTERSECTION POINT.
4) Reverse first linked list.
Advantage: No Comparison of pointers.
Disadvantage : Modifying linked list(Reversing list).
Time complexity: O(m+n)
Auxiliary Space: O(1)
Method 6 (Store the first node and reverse the first list) This method is only to detect if there is an intersection point or not. Below algorithm will return 1 if there is an intersection else 0. (Thanks to Hari Prasad Perabattula for suggesting this)
1). Store the header node of list l1.
2). Reverse the first list l1.
3). Traverse the second list until reaches NULL.
4). Check if the node just before NULL is the same as the header in step (1).
If it is then there is an intersection(return 1), otherwise not (return 0).
5).Reverse the list l1 to make it restore the original list.
Time complexity of this method is O(m+n) and used Auxiliary space is O(1)
(Continued on next question...)
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