Summary of "L26. Print Root to Node Path in Binary Tree | C++ | Java"

Summary of “L26. Print Root to Node Path in Binary Tree | C++ | Java”

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Main Ideas and Concepts

• The video addresses the problem of printing the path from the root node to a given target node in a binary tree.
• It distinguishes between root-to-node path and root-to-leaf path problems.
• The main challenge is to find the path without using parent pointers.
• The solution employs a recursive traversal approach (specifically inorder traversal).
• Backtracking is used by adding nodes to a path array when traversing down and removing them if the target is not found in that subtree.
• The approach is demonstrated with both C++ and Java code, which are very similar.

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Detailed Explanation and Methodology

Problem Statement

Given a binary tree and a target node (or target node value), find the path from the root to that node.

• Example: For target node 7, the path might be [1, 2, 5, 7].

Key Challenge

• No parent pointers are available to traverse upwards.
• The path must be found using tree traversal and recursion.

Why Inorder Traversal?

• Although preorder or postorder traversal could be used, inorder traversal is simpler to implement and explain, especially in interviews.
• Emphasis is on simplicity in explanation and implementation.

Step-by-step Recursive Approach

1. Start at the root node.
2. Use a data structure (like an array or vector) to keep track of the current path.
3. Add the current node to the path array.
4. If the current node matches the target node, return true immediately (path found).
5. Recursively search the left subtree:
   • If the left subtree returns true, propagate true upwards without removing the current node.
6. If the left subtree returns false, recursively search the right subtree:
   • If the right subtree returns true, propagate true upwards.
7. If neither subtree contains the target node, remove the current node from the path array (backtracking) and return false.
8. Continue this process until the target node is found or the entire tree is traversed.

Important Details

• The path array is passed by reference to maintain state across recursive calls.
• Backtracking (removing nodes) ensures only the correct path remains.
• Once the target node is found, recursion unwinds without removing nodes on the correct path.

Code Implementation Highlights

• Initialize an empty vector/array to store the path.
• If the root is null, return false or indicate no path.
• Use a helper function getPath(root, path, target):
  • Returns true if the target is found in the subtree rooted at root.
  • Updates path accordingly.
• After the function completes, path contains the root-to-target path.
• Time complexity: O(N), where N is the number of nodes (each node visited once).
• Space complexity: O(H), where H is the height of the tree (due to recursion stack and path storage).

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Summary of Steps (Methodology)

Print Root to Node Path Algorithm

• Input: root (root of binary tree), target (node value or node)
• Output: Array/list representing path from root to target node

Algorithm:

• Create an empty list path.
• Define recursive function getPath(node, path, target):
  • If node is null, return false.
  • Add node to path.
  • If node matches target, return true.
  • Recursively call getPath on left child:
    • If returns true, return true.
  • Recursively call getPath on right child:
    • If returns true, return true.
  • If neither subtree contains target:
    • Remove node from path (backtrack).
    • Return false.
• Call getPath(root, path, target).
• path now contains the root-to-target node path.

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Speakers / Sources

• The video is presented by a single instructor (name not provided).
• The sponsor mentioned is “Reliable by an Academy”, a hiring platform for freshers and experienced candidates.
• The instructor provides explanations and code in both C++ and Java.

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Final Notes

• The video emphasizes clarity and simplicity in explaining the solution.
• Viewers are encouraged to like, comment, and subscribe for more tutorials.
• The approach can be adapted to similar problems like root-to-leaf path.
• The code and explanation cover edge cases such as null nodes and missing targets (though the problem assumes the target exists).

Category

Educational

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