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How to Solve Find Valid Matrix Given Row and Column Sums Problem
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Start Free for WindowsMedium#1605
LeetCode ProblemFind Valid Matrix Given Row and Column Sums
You are given two arrays rowSum and colSum of non-negative integers where rowSum[i] is the sum of the elements in the ith row and colSum[j] is the sum of the elements of the jth column of a 2D matrix. In other words, you do not know the elements of the matrix, but you do know the sums of each row and column. Find any matrix of non-negative integers of size rowSum.length x colSum.length that satisfies the rowSum and colSum requirements. Return a 2D array representing any matrix that fulfills the requirements. It's guaranteed that at least one matrix that fulfills the requirements exists.
ArrayGreedyMatrix
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Problem Breakdown
Understanding the Find Valid Matrix Given Row and Column Sums Problem
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Problem Statement
You are given two arrays rowSum and colSum of non-negative integers where rowSum[i] is the sum of the elements in the ith row and colSum[j] is the sum of the elements of the jth column of a 2D matrix. In other words, you do not know the elements of the matrix, but you do know the sums of each row and column. Find any matrix of non-negative integers of size rowSum.length x colSum.length that satisfies the rowSum and colSum requirements. Return a 2D array representing any matrix that fulfills the requirements. It's guaranteed that at least one matrix that fulfills the requirements exists.
MediumProblem #1605
LeetCodeFind Valid Matrix Given Row and Column Sums
Related Topics
ArrayGreedyMatrix
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Examples
# Example 1
Input
rowSum = [3,8], colSum = [4,7]
Output
[[3,0], [1,7]]
# Example 2
Input
rowSum = [5,7,10], colSum = [8,6,8]
Output
[[0,5,0], [6,1,0], [2,0,8]]
Constraints
1 <= rowSum.length, colSum.length <= 500
0 <= rowSum[i], colSum[i] <= 108
sum(rowSum) == sum(colSum)
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Solve Find Valid Matrix Given Row and Column Sums — You are given two arrays rowSum and colSum of non-negative integers where rowSum...
Here's the optimal approach using Array:
def solve(input):
# Optimal O(n) solution
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Time: O(n) | Space: O(n)
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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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