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The idea is to reverse the second half of the linked list and utilize the twin's definition by simultaneously traversing the first half from the beginning and the reversed half from the end. As you do this traversal, calculate the twin sums and keep track of the maximum.
Steps:
Time Complexity: O(n), where n is the number of nodes in the linked list.
Space Complexity: O(1), as we only use a constant amount of additional space.
1class ListNode:
2 def __init__(self, val=0, next=None):
3 self.val = val
4 self.next = next
This solution in Python finds the middle of the linked list, reverses the second half, computes the twin sums, and returns the greatest twin sum found.
This approach makes use of an auxiliary array where we store the values of the linked list nodes. Once stored, we can leverage the structure of the list to easily compute twin sums using simple array indexing.
Steps:
Time Complexity: O(n).
Space Complexity: O(n), due to the auxiliary array.
This solution uses a JavaScript array to first capture linked list values, enabling swift evaluation of the maximum twin sum through array indices.