Approach
Direct simulation
For Maximum Side Length of a Square with Sum Less than or Equal to Threshold, the implementation follows the problem’s operations directly while maintaining only the state needed for the next decision.
- Translate each rule into one explicit state update.
- Maintain the invariant after every processed item.
- Return the accumulated state once all relevant input has been handled.
Code notes
- 27 lines of Java from the credited upstream file 1292.java.
- The implementation visibly relies on sequence storage.
- 5 loop blocks detected.
Complexity
Count the number and nesting of passes over the input, then include the maintained containers in the memory estimate.
Check the problem constraints before deciding whether this complexity will pass.
Use this to learn the idea, then write your own version.
1class Solution {2 public int maxSideLength(int[][] mat, int threshold) {3 final int m = mat.length;4 final int n = mat[0].length;5 int ans = 0;6 int[][] prefix = new int[m + 1][n + 1];7 8 for (int i = 0; i < m; ++i)9 for (int j = 0; j < n; ++j)10 prefix[i + 1][j + 1] = mat[i][j] + prefix[i][j + 1] + prefix[i + 1][j] - prefix[i][j];11 12 for (int i = 0; i < m; ++i)13 for (int j = 0; j < n; ++j)14 for (int length = ans; length < Math.min(m - i, n - j); ++length) {15 if (squareSum(prefix, i, j, i + length, j + length) > threshold)16 break;17 ans = Math.max(ans, length + 1);18 }19 20 return ans;21 }22 23 private int squareSum(int[][] prefix, int r1, int c1, int r2, int c2) {24 return prefix[r2 + 1][c2 + 1] - prefix[r1][c2 + 1] - prefix[r2 + 1][c1] + prefix[r1][c1];25 }26}27