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How to Solve Maximum Product of Splitted Binary Tree Problem
Master the Maximum Product of Splitted Binary Tree LeetCode problem with undetectable real-time assistance. Get instant solutions and explanations during your coding interviews.
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Start Free for WindowsMedium#1339
LeetCode ProblemMaximum Product of Splitted Binary Tree
Given the root of a binary tree, split the binary tree into two subtrees by removing one edge such that the product of the sums of the subtrees is maximized. Return the maximum product of the sums of the two subtrees. Since the answer may be too large, return it modulo 109 + 7. Note that you need to maximize the answer before taking the mod and not after taking it.
TreeDepth-First SearchBinary Tree
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Problem Breakdown
Understanding the Maximum Product of Splitted Binary Tree Problem
Let's break down this LeetCode problem and understand what makes it challenging in interview settings.
Problem Statement
Given the root of a binary tree, split the binary tree into two subtrees by removing one edge such that the product of the sums of the subtrees is maximized. Return the maximum product of the sums of the two subtrees. Since the answer may be too large, return it modulo 109 + 7. Note that you need to maximize the answer before taking the mod and not after taking it.
MediumProblem #1339
LeetCodeMaximum Product of Splitted Binary Tree
Related Topics
TreeDepth-First SearchBinary Tree
How Phantom Code Helps
Get real-time assistance for Maximum Product of Splitted Binary Tree problems during coding interviews. Phantom Code provides instant solutions and explanations.
Examples
# Example 1
Input
root = [1,2,3,4,5,6]
Output
110
# Example 2
Input
root = [1,null,2,3,4,null,null,5,6]
Output
90
Constraints
The number of nodes in the tree is in the range [2, 5 * 104].
1 <= Node.val <= 104
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Solve Maximum Product of Splitted Binary Tree — Given the root of a binary tree, split the binary tree into two subtrees by remo...
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def solve(input):
# Optimal O(n) solution
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Frequently Asked Questions
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Phantom Code catches common mistakes like off-by-one errors, missing edge cases, incorrect base conditions, and suboptimal approaches. It provides clean, well-structured code that handles all edge cases.
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The AI provides structured guidance: first the approach (what algorithm/data structure to use and why), then the implementation with clean code, and finally complexity analysis. This helps you think clearly even under pressure.
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