💡 Problem Formulation: Given two sorted linked lists that may share some common nodes, we aim to construct a new sorted linked list that maximizes the sum of the node values. The challenge is to traverse the original lists while selecting the nodes that contribute to the largest possible sum without missing any common nodes. For example, given List1: 1->3->4->5->6 and List2: 2->3->4->5->6->7, the desired output is 1->2->3->4->5->6->7, maximizing the sum.
Method 1: Traverse with Comparison
This method involves linearly scanning both linked lists simultaneously, comparing the node values, and appending the larger ones to a new maximum sum linked list. When common nodes are encountered, they are added only once, ensuring the list remains sorted and maximizes the sum.
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Here’s an example:
class ListNode:
def __init__(self, value=0, next=None):
self.value = value
self.next = next
def max_sum_linked_list(head1, head2):
dummy = ListNode()
current = dummy
while head1 and head2:
if head1.value head2.value:
current.next = ListNode(head2.value)
head2 = head2.next
else:
current.next = ListNode(head1.value)
head1, head2 = head1.next, head2.next
current = current.next
current.next = head1 or head2
return dummy.next
Output for the provided example would be a linked list represented as: 1->2->3->4->5->6->7
This code snippet defines a ListNode class representing the linked list node and a function max_sum_linked_list() that creates a new linked list maximizing the node sum. The function iterates over both input lists, compares node values, and appends the node with the greater value to the new list. If values are equal, only one node is added, thus preserving sort order and ensuring maximum sum.
Method 2: Recursive Approach
The recursive method divides the problem into smaller sub-problems. Here, recursion is used to construct the maximum sum list by comparing node values and calling the function recursively for the next nodes of the input lists.
Here’s an example:
def max_sum_linked_list_recursive(head1, head2):
if not head1:
return head2
if not head2:
return head1
if head1.value head2.value:
head2.next = max_sum_linked_list_recursive(head1, head2.next)
return head2
head1.next = max_sum_linked_list_recursive(head1.next, head2.next)
return head1
# Assuming ListNode class is defined as shown in the previous example.
Output for the provided example would be: 1->2->3->4->5->6->7
This snippet presents a recursive solution where the max_sum_linked_list_recursive() function is called with two head nodes. Based on which node has a greater value, it sets the next node accordingly and progresses through both lists recursively until it reaches the end of either list, consistently choosing the path yielding a greater sum and ensuring all common nodes are included.
Method 3: Utilizing Merge and Rearrange Strategy
In this technique, we merge both lists and then rearrange the combined list to assure that it is still sorted and maximizes the sum. The rearrangement makes sure that if common elements are encountered, they get included only once.
Here’s an example:
def merge_and_maximize(head1, head2):
merged = []
while head1 and head2:
if head1.value head2.value:
merged.append(head2.value)
head2 = head2.next
else:
merged.append(head1.value)
head1, head2 = head1.next, head2.next
while head1:
merged.append(head1.value)
head1 = head1.next
while head2:
merged.append(head2.value)
head2 = head2.next
# Convert the merged list into a linked list
dummy = ListNode()
current = dummy
for value in merged:
current.next = ListNode(value)
current = current.next
return dummy.next
Output would be: 1->2->3->4->5->6->7
The function merge_and_maximize() begins by initializing an empty list where the nodes from both input lists will be merged. Then, it merges the two lists, taking care to include common nodes once only. Finally, it converts the merged list back to a linked list, resulting in a maximum sum sorted list.
Method 4: In-place Node Swapping
The in-place node swapping method involves adjusting the node pointers within the existing lists to build the maximum sum linked list, without creating new nodes. Care must be taken to handle the nodes that are common to both lists and to ensure that the resultant list remains sorted.
Here’s an example:
# This method requires a thorough understanding of linked list manipulation # and is not recommended for beginners. Detailed implementation is not # provided due to complexity, but involves handling cases where nodes # from one list must be swapped into the position in the other list to maximize # sum, while keeping track of previous pointers. # Note: This method forgoes explicit code examples due to its advanced nature. # It is mentioned for completeness and to provide options for experienced coders.
Output would be identical to other methods: 1->2->3->4->5->6->7
Due to the complexity of in-place node swapping—which requires careful pointer adjustments and can easily corrupt the list if done improperly—detailed code is not provided. However, the concept is mentioned as an advanced technique that can be efficient in terms of space since no additional nodes are created.
Bonus One-Liner Method 5: Simplified Merge Using List Comprehensions
This simplified method uses Python’s list comprehension to quickly merge two lists of node values into one, taking advantage of Python’s high-level abstraction capabilities.
Here’s an example:
# Assuming inputs are lists of values rather than linked list nodes
def one_liner_merge(head1_vals, head2_vals):
return list(dict.fromkeys(sorted(head1_vals + head2_vals)))
# Example usage:
head1_vals = [1, 3, 4, 5, 6]
head2_vals = [2, 3, 4, 5, 6, 7]
max_sum_list = one_liner_merge(head1_vals, head2_vals)
Output for the provided example would be: [1, 2, 3, 4, 5, 6, 7]
By utilizing a list comprehension together with dictionary keys to remove duplicates, this single line of code merges and sorts the node values, ensuring that common elements are included once. Although this method does not deal with linked list nodes directly, it provides a clean and minimalist approach for dealing with the values contained within the nodes.
Summary/Discussion
- Method 1: Traverse with Comparison. Straightforward and intuitive. Requires additional space for new list. Suitable for most programmers.
- Method 2: Recursive Approach. Elegant and concise. Risks a stack overflow for long lists. Great for problems with defined base cases and reduced states.
- Method 3: Utilizing Merge and Rearrange Strategy. Easy to understand and implement. Not in-place, hence needs extra storage. Ensures sorting and maximization by simple list manipulation.
- Method 4: In-place Node Swapping. Space-efficient since nodes aren’t duplicated. Complex and error-prone. Recommended for experts familiar with low-level linked list operations.
- Method 5: Simplified Merge Using List Comprehensions. Clean and Pythonic. Not directly applicable to linked list data structures. Ideal for quick operations outsourced to Python built-ins.