Off-by-one on range boundaries
Wrong move: Loop endpoints miss first/last candidate.
Usually fails on: Fails on minimal arrays and exact-boundary answers.
Fix: Re-derive loops from inclusive/exclusive ranges before coding.
Move from brute-force thinking to an efficient approach using array strategy.
Given two integer arrays, preorder and postorder where preorder is the preorder traversal of a binary tree of distinct values and postorder is the postorder traversal of the same tree, reconstruct and return the binary tree.
If there exist multiple answers, you can return any of them.
Example 1:
Input: preorder = [1,2,4,5,3,6,7], postorder = [4,5,2,6,7,3,1] Output: [1,2,3,4,5,6,7]
Example 2:
Input: preorder = [1], postorder = [1] Output: [1]
Constraints:
1 <= preorder.length <= 301 <= preorder[i] <= preorder.lengthpreorder are unique.postorder.length == preorder.length1 <= postorder[i] <= postorder.lengthpostorder are unique.preorder and postorder are the preorder traversal and postorder traversal of the same binary tree.Problem summary: Given two integer arrays, preorder and postorder where preorder is the preorder traversal of a binary tree of distinct values and postorder is the postorder traversal of the same tree, reconstruct and return the binary tree. If there exist multiple answers, you can return any of them.
Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.
Pattern signal: Array · Hash Map · Tree
[1,2,4,5,3,6,7] [4,5,2,6,7,3,1]
[1] [1]
Source-backed implementations are provided below for direct study and interview prep.
// Accepted solution for LeetCode #889: Construct Binary Tree from Preorder and Postorder Traversal
/**
* Definition for a binary tree node.
* public class TreeNode {
* int val;
* TreeNode left;
* TreeNode right;
* TreeNode() {}
* TreeNode(int val) { this.val = val; }
* TreeNode(int val, TreeNode left, TreeNode right) {
* this.val = val;
* this.left = left;
* this.right = right;
* }
* }
*/
class Solution {
private Map<Integer, Integer> pos = new HashMap<>();
private int[] preorder;
public TreeNode constructFromPrePost(int[] preorder, int[] postorder) {
this.preorder = preorder;
for (int i = 0; i < postorder.length; ++i) {
pos.put(postorder[i], i);
}
return dfs(0, preorder.length - 1, 0, postorder.length - 1);
}
private TreeNode dfs(int a, int b, int c, int d) {
if (a > b) {
return null;
}
TreeNode root = new TreeNode(preorder[a]);
if (a == b) {
return root;
}
int i = pos.get(preorder[a + 1]);
int m = i - c + 1;
root.left = dfs(a + 1, a + m, c, i);
root.right = dfs(a + m + 1, b, i + 1, d - 1);
return root;
}
}
// Accepted solution for LeetCode #889: Construct Binary Tree from Preorder and Postorder Traversal
/**
* Definition for a binary tree node.
* type TreeNode struct {
* Val int
* Left *TreeNode
* Right *TreeNode
* }
*/
func constructFromPrePost(preorder []int, postorder []int) *TreeNode {
pos := map[int]int{}
for i, x := range postorder {
pos[x] = i
}
var dfs func(int, int, int, int) *TreeNode
dfs = func(a, b, c, d int) *TreeNode {
if a > b {
return nil
}
root := &TreeNode{Val: preorder[a]}
if a == b {
return root
}
i := pos[preorder[a+1]]
m := i - c + 1
root.Left = dfs(a+1, a+m, c, i)
root.Right = dfs(a+m+1, b, i+1, d-1)
return root
}
return dfs(0, len(preorder)-1, 0, len(postorder)-1)
}
# Accepted solution for LeetCode #889: Construct Binary Tree from Preorder and Postorder Traversal
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution:
def constructFromPrePost(
self, preorder: List[int], postorder: List[int]
) -> Optional[TreeNode]:
def dfs(a: int, b: int, c: int, d: int) -> Optional[TreeNode]:
if a > b:
return None
root = TreeNode(preorder[a])
if a == b:
return root
i = pos[preorder[a + 1]]
m = i - c + 1
root.left = dfs(a + 1, a + m, c, i)
root.right = dfs(a + m + 1, b, i + 1, d - 1)
return root
pos = {x: i for i, x in enumerate(postorder)}
return dfs(0, len(preorder) - 1, 0, len(postorder) - 1)
// Accepted solution for LeetCode #889: Construct Binary Tree from Preorder and Postorder Traversal
struct Solution;
use rustgym_util::*;
impl Solution {
fn construct_from_pre_post(pre: Vec<i32>, post: Vec<i32>) -> TreeLink {
Self::build(&mut 0, &mut 0, &pre, &post)
}
fn build(i: &mut usize, j: &mut usize, pre: &[i32], post: &[i32]) -> TreeLink {
let val = pre[*i];
*i += 1;
let mut left = None;
let mut right = None;
if val != post[*j] {
left = Self::build(i, j, pre, post);
}
if val != post[*j] {
right = Self::build(i, j, pre, post);
}
*j += 1;
tree!(val, left, right)
}
}
#[test]
fn test() {
let pre = vec![1, 2, 4, 5, 3, 6, 7];
let post = vec![4, 5, 2, 6, 7, 3, 1];
let res = tree!(
1,
tree!(2, tree!(4), tree!(5)),
tree!(3, tree!(6), tree!(7))
);
assert_eq!(Solution::construct_from_pre_post(pre, post), res);
}
// Accepted solution for LeetCode #889: Construct Binary Tree from Preorder and Postorder Traversal
/**
* Definition for a binary tree node.
* class TreeNode {
* val: number
* left: TreeNode | null
* right: TreeNode | null
* constructor(val?: number, left?: TreeNode | null, right?: TreeNode | null) {
* this.val = (val===undefined ? 0 : val)
* this.left = (left===undefined ? null : left)
* this.right = (right===undefined ? null : right)
* }
* }
*/
function constructFromPrePost(preorder: number[], postorder: number[]): TreeNode | null {
const pos: Map<number, number> = new Map();
const n = postorder.length;
for (let i = 0; i < n; ++i) {
pos.set(postorder[i], i);
}
const dfs = (i: number, j: number, n: number): TreeNode | null => {
if (n <= 0) {
return null;
}
const root = new TreeNode(preorder[i]);
if (n === 1) {
return root;
}
const k = pos.get(preorder[i + 1])!;
const m = k - j + 1;
root.left = dfs(i + 1, j, m);
root.right = dfs(i + 1 + m, k + 1, n - 1 - m);
return root;
};
return dfs(0, 0, n);
}
Use this to step through a reusable interview workflow for this problem.
BFS with a queue visits every node exactly once — O(n) time. The queue may hold an entire level of the tree, which for a complete binary tree is up to n/2 nodes = O(n) space. This is optimal in time but costly in space for wide trees.
Every node is visited exactly once, giving O(n) time. Space depends on tree shape: O(h) for recursive DFS (stack depth = height h), or O(w) for BFS (queue width = widest level). For balanced trees h = log n; for skewed trees h = n.
Review these before coding to avoid predictable interview regressions.
Wrong move: Loop endpoints miss first/last candidate.
Usually fails on: Fails on minimal arrays and exact-boundary answers.
Fix: Re-derive loops from inclusive/exclusive ranges before coding.
Wrong move: Zero-count keys stay in map and break distinct/count constraints.
Usually fails on: Window/map size checks are consistently off by one.
Fix: Delete keys when count reaches zero.
Wrong move: Recursive traversal assumes children always exist.
Usually fails on: Leaf nodes throw errors or create wrong depth/path values.
Fix: Handle null/base cases before recursive transitions.