Approach

Merging k sorted linked lists into a single sorted linked list is a common problem in computer science and coding interviews. To answer this question effectively, follow a structured framework:

1. Understand the Problem: Clearly define the input and output requirements.

2. Choose an Optimal Method: Evaluate different algorithms to find the most efficient solution.

3. Explain Your Thought Process: Walk through your reasoning and decision-making.

4. Provide a Sample Implementation: Present code that showcases your solution.

5. Discuss Complexity: Analyze the time and space complexity of your approach.

Key Points

• Clarity: Interviewers look for clear explanations and logical reasoning.

• Efficiency: Highlight the efficiency of your chosen algorithm, especially in terms of time complexity.

• Edge Cases: Consider edge cases, such as empty lists or lists with varying lengths.

• Communication: Articulate your thought process clearly throughout the discussion.

Standard Response

Sample Answer:

To merge k sorted linked lists into a single sorted linked list, I would opt for a min-heap (or priority queue) approach. This method efficiently handles the merging process while maintaining a low time complexity.

• Understanding the Problem:

• We have k linked lists, each sorted in ascending order.

• Our goal is to combine these lists into one sorted linked list.

• Optimal Method:

• I would use a min-heap to keep track of the smallest elements across all k lists.

• By repeatedly extracting the minimum element from the heap and adding it to the merged list, we can ensure that the merged list remains sorted.

• Implementation:

Here’s a Python implementation of the approach:

 import heapq

 class ListNode:
 def __init__(self, value=0, next=None):
 self.value = value
 self.next = next

 def mergeKLists(lists):
 min_heap = []
 for index, linked_list in enumerate(lists):
 if linked_list:
 heapq.heappush(min_heap, (linked_list.value, index, linked_list))

 dummy = ListNode(0)
 current = dummy

 while min_heap:
 value, index, node = heapq.heappop(min_heap)
 current.next = node
 current = current.next
 if node.next:
 heapq.heappush(min_heap, (node.next.value, index, node.next))

 return dummy.next

• Complexity Analysis:

• Time Complexity: The time complexity is O(N log k), where N is the total number of nodes across all k lists. Each node is processed once, and we perform log k operations for each insertion and deletion in the min-heap.

• Space Complexity: The space complexity is O(k) for the min-heap.

This approach is efficient and straightforward, making it suitable for handling large inputs.

Tips & Variations

• Failing to consider edge cases, such as empty linked lists.

• Not explaining the reasoning behind the chosen algorithm.

• Ignoring the time and space complexity analysis.

• Common Mistakes to Avoid:

• Brute Force Approach: Collect all nodes from the k lists into an array, sort it, and then create a new linked list from the sorted array. However, this is less efficient than the min-heap method.

• Divide and Conquer: Merge pairs of lists recursively until only one list remains. This method has a time complexity of O(N log k) and is also efficient.

• Alternative Ways to Answer:

• Technical Roles: Focus on code optimization and edge cases.

• Managerial Roles: Emphasize leadership in problem-solving and team collaboration during implementation.

• Creative Roles: Discuss innovative approaches to data handling, even if less conventional.

• Role-Specific Variations:

• Can you explain how you would handle very large linked lists that don’t fit into memory?

• What would be your approach if the linked lists were not guaranteed to be sorted?

• How would you modify your solution for a multithreaded environment?

• Follow-Up Questions:

By structuring your response following this guide, you can effectively demonstrate your problem-solving abilities and technical knowledge during your interview