answer1.md

Question 1

Write a function that takes two arrays as input, each array contains a list of A-Z; Your program should return True if the 2nd array is a subset of 1st array, or False if not.

For example:
isSubset([A,B,C,D,E], [A,E,D]) = true
isSubset([A,B,C,D,E], [A,D,Z]) = false
isSubset([A,D,E], [A,A,D,E]) = true

Please explain the computational complexity of your answer in Big-O notation, i.e. O(log n) or O(n ^2)?

Answer A

Time complexity: O(n^2) (The nested loops)
Space complexity: O(1) (isFound)

# Assume arrays input
def isSubset(arr1, arr2):
  for item2 in arr2:  
    isFound = False
  
    for item1 in arr1:    
      if item1 == item2:
        isFound = True
        break
  
    if not isFound:
      return False
  
  return True

If it's under tight memory constraint, this is the preferred solution.

Answer B

Time complexity: O(n) (The loops, O(n1) + O(n2))
Space complexity: O(n) (the hash map)

# Assume arrays input
def isSubset(arr1, arr2):
  arr1Map = {item: True for item in arr1}
  
  for item in arr2:
    try:
      arr1Map[item]
    except KeyError:
      return False
  
  return True

If the input arrays are exceptional large and time efficiency is of higher priority, this is the preferred solution.

Test Cases

Please find them at Repl.it