#Matrix Cover


    # Initialize a list to track the covered columns

    # Iterate over the rows, selecting the row that covers the maximum number of uncovered columns at each step

                # Count the number of uncovered columns for this row


        # If there is a row that covers at least one uncovered column, mark those columns as covered

    # Find the indices of the covered columns


# Example usage
    # Example matrix

    # Solve the matrix cover problem

    # Print the result


if __name__ == '__main__':
    def matrix_cover(matrix):
        num_rows = len(matrix)
        num_cols = len(matrix[0])
        covered_cols = [False] * num_cols
        while not all(covered_cols):
            max_covered_count = 0
            max_covered_row = None
            for row in range(num_rows):
                if not all(matrix[row][col] or covered_cols[col] for col in range(num_cols)):
                    covered_count = sum(not matrix[row][col] and not covered_cols[col] for col in range(num_cols))
                    if covered_count > max_covered_count:
                        max_covered_count = covered_count
                        max_covered_row = row
            if max_covered_row is not None:
                for col in range(num_cols):
                    if not matrix[max_covered_row][col]:
                        covered_cols[col] = True
        covered_indices = [index for index, covered in enumerate(covered_cols) if covered]
        return covered_indices
    if __name__ == '__main__':
        matrix = [
            [1, 0, 1, 0, 0],
            [1, 1, 0, 0, 1],
            [0, 1, 1, 1, 0],
            [0, 0, 1, 0, 0],
            [1, 1, 0, 1, 1]
        ]
        covered_indices = matrix_cover(matrix)
        print("Covered Columns:", covered_indices)
