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In this approach, we utilize a stack to achieve depth-first traversal of the multilevel doubly linked list. We push nodes into the stack starting from the head, along with managing the child nodes as higher priority over next nodes. This ensures that we process all child nodes before moving on to the next nodes.
Time Complexity: O(n) where n is the number of nodes. Each node is visited once.
Space Complexity: O(n) for the stack in the worst case scenario.
1function flatten(head) {
2 if (!head) return head;
3
4 let stack = [];
5 let curr = head;
6
7 while (curr) {
8 if (curr.child) {
9 if (curr.next) stack.push(curr.next);
10 curr.next = curr.child;
11 curr.next.prev = curr;
12 curr.child = null;
13 }
14
15 if (!curr.next && stack.length > 0) {
16 curr.next = stack.pop();
17 if (curr.next) curr.next.prev = curr;
18 }
19
20 curr = curr.next;
21 }
22
23 return head;
24}This JavaScript solution derives its logical flow from stack usage to dictate when to return to previously visited nodes, ensuring flattening of children before moving down the list. The language's flexibility aids in handling node connections and assignments elegantly.
This approach utilizes recursion to handle the traversing and flattening of lists. By inherently using the function call stack, it efficiently manages shifts between the parent and child lists, automatically flattening the entire structure as it recursively resolves each node and its children.
Time Complexity: O(n) due to the necessity to visit each node once.
Space Complexity: O(d) where d is the maximum depth of the children, necessitating stack space for recursion.
The Python approach leverages recursion through 'flattenDFS' to break down the multilevel nodes into simpler, flat arrangements, dynamically patching lists together by handling next node linkage.