An algorithm that takes two possible candidate solutions as input, and returns the best possible candidate solution is called Compare algorithm Exhaustive search algorithm Verifier algorithm

def solve_n_queens(n):    def is_safe(board, row, col):        # Verificar la columna        for i in range(row):            if board[i][col] == 1:                return False               # Verificar la diagonal izquierda        i, j = row, col        while i >= 0 and j >= 0:            if board[i][j] == 1:                return False            i -= 1            j -= 1        # Verificar la diagonal derecha        i, j = row, col        while i >= 0 and j < n:            if board[i][j] == 1:                return False            i -= 1            j += 1        return True    def solve(board, row):        if row >= n:            solution = []            for r in board:                solution.append("".join(['R' if x == 1 else '.' for x in r]))            solutions.append(solution)            return               for col in range(n):            if is_safe(board, row, col):                board[row][col] = 1                solve(board, row + 1)…

Is there a reason why binary search is so more faster than linear search? Explain why what you did was right.

Algorithm Binary Search Input:  L = a1, a2, ... , an list of distinct integers; x integer Output: p integer i = 1; j = n; found = F; p = 0; while i ≤ j and found = F   m = ⌊i+j2⌋; if am = x then found = T and p = m             else if am < x then i = m + 1                                    else j = m - 1; return p; When performing Algorithm Binary Search with input  L = 1,3,5,7,9,11,13 and x = 9, how many times is x compared to an element ai of the list L?