Consider the routine below which sorts the input array (very inefficiently). Determine the run-time complexity of the routine using Big-O notation.

def slow_sort(myArray):
  n = length(myArray)
  sorted_array = new array, same length as myArray
  for i in [0, 1, ..., n - 1]:
    min_val = array[0]
    min_location = 0
    for j in [0, 1, ..., length(myArray) - 1]:
      if array[j] < min_val:
        min_val = myArray[j]
        min_location = j

    sorted[i] = min_val
    delete myArray[min_location] #reduces the size of the input array

  return sorted_array

The algorithm as written above has a run-time complexity of Select an answer O(n^2) O(n!) O(n^3) O(1) O(2^n) O(n) O(log(n))