# My LeetCode Journey -- BST 3. Delete Node

draft-post/TRANSFERRED-2019-12-28 Fang

The recursive algorithm introduced here is much simpler than what was described in the last post.

Problem: 450. Delete Node in a BST.

## Cases

• Node is a leaf, and one could delete it straightforward : `node = null`.
• Node is not a leaf and has a right child. Then the node could be replaced by its successor which is somewhere lower in the right subtree. Then one could proceed down recursively to delete the successor.
• Node is not a leaf, has no right child and has a left child. That means that its successor is somewhere upper in the tree but we don’t want to go back. Let’s use the predecessor here which is somewhere lower in the left subtree. The node could be replaced by its predecessor and then one could proceed down recursively to delete the predecessor.

## Successor & Predecessor

Given that successor exists in the right subtree, it can be obtained by descend right once and then as many times to the left as you could.

Similarly, to find a predecessor, go to the left once and then as many times to the right as you could.

## Algorithm

• If key > root.val then delete the node to delete is in the right subtree root.right = deleteNode(root.right, key).
• If key < root.val then delete the node to delete is in the left subtree root.left = deleteNode(root.left, key).
• If key == root.val then the node to delete is right here. Let’s do it :

• If the node is a leaf, the delete process is straightforward : root = null.
• If the node is not a leaf and has the right child, then replace the node value by a successor value root.val = successor.val, and then recursively delete the successor in the right subtree root.right = deleteNode(root.right, root.val).
• If the node is not a leaf and has only the left child, then replace the node value by a predecessor value root.val = predecessor.val, and then recursively delete the predecessor in the left subtree root.left = deleteNode(root.left, root.val).
• Return root.

## Implementation

``````/**
* Definition for a binary tree node.
* public class TreeNode {
*     int val;
*     TreeNode left;
*     TreeNode right;
*     TreeNode(int x) { val = x; }
* }
*/
class Solution {
/*
One step right and then always left
*/
public int successor(TreeNode root) {
root = root.right;
while (root.left != null) root = root.left;
return root.val;
}

/*
One step left and then always right
*/
public int predecessor(TreeNode root) {
root = root.left;
while (root.right != null) root = root.right;
return root.val;
}

public TreeNode deleteNode(TreeNode root, int key) {
if (root == null) return null;

// delete from the right subtree
if (key > root.val) root.right = deleteNode(root.right, key);
// delete from the left subtree
else if (key < root.val) root.left = deleteNode(root.left, key);
// delete the current node
else {
// the node is a leaf
if (root.left == null && root.right == null) root = null;
// the node is not a leaf and has a right child
else if (root.right != null) {
root.val = successor(root);
root.right = deleteNode(root.right, root.val);
}
// the node is not a leaf, has no right child, and has a left child
else {
root.val = predecessor(root);
root.left = deleteNode(root.left, root.val);
}
}
return root;
}
}``````

## Complexity

• Time complexity : `O(logN)`.

• First to search the node to delete costs `O(H_1)`, where `H_1` is the tree height from the root to the node to delete
• Delete process takes `O(H_2)` time, where `H_2` is a tree height from the root to delete to the leafs. That in total results in `O(H_1 + H_2) =O(H)` time complexity, where `H` is a tree height, equal to `logN` in the case of the balanced tree.
• Space complexity :`O(H)` to keep the recursion stack, where `H` is a tree height. `H = logN` for the balanced tree.