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This approach involves two main steps. First, find the minimum element in each row and keep track of the potential lucky numbers. Then, verify these potential numbers to check if they are the maximum in their respective columns.
Time Complexity: O(m * n) where m is the number of rows and n is the number of columns in the matrix.
Space Complexity: O(m) for storing the minimum indices for each row.
1var luckyNumbers = function(matrix) {
2 let minRow = matrix.map(row => Math.min(...row));
3 let maxCol = Array(matrix[0].length).fill(Number.MIN_SAFE_INTEGER);
4
5 for (let i = 0; i < matrix.length; i++) {
6 for (let j = 0; j < matrix[i].length; j++) {
7 maxCol[j] = Math.max(maxCol[j], matrix[i][j]);
8 }
9 }
10
11 return minRow.filter(val => maxCol.includes(val));
12};The JavaScript solution uses a two-step approach to identify row minima across the entire row, then the maximum from the entire column.
This approach leverages set operations from mathematics to identify potential lucky numbers. We extract the row minimums and column maximums into separate sets and find the intersection of these sets for possible lucky numbers.
Time Complexity: O(m * n) where m is the number of rows and n is the number of columns.
Space Complexity: O(n) for storing column maximums.
1We utilize Python's powerful collection tools to directly find intersections between row minima and column maxima, yielding possible lucky numbers efficiently.