input: Write a function to find the optimal solution to solve a knapsack problem using dynamic programming.
  
  ### Input:
  Given Items (with values and weights): [(60, 10), (100, 20), (120, 30)] 
  Capacity of Knapsack: 50
  
  ### Output:
  def knapSack(W, wt, val, n):
    
      # Base Case 
      if n == 0 or W == 0 : 
          return 0
    
      # If weight of the nth item is more 
      # than Knapsack capacity W, then 
      # this item cannot be included in the optimal solution 
      if (wt[n-1] > W): 
          return knapSack(W, wt, val, n-1) 
    
      # return the maximum of two cases: 
      # (1) nth item included 
      # (2) not included 
      else: 
          return max(val[n-1] + knapSack(W-wt[n-1], wt, val, n-1), 
                      knapSack(W, wt, val, n-1)) 
  
  assert knapSack(50, [10, 20, 30], [60, 100, 120], 3)==220
  assert knapSack(60, [10, 20, 30], [60, 100, 120], 3)==220
  assert knapSack(40, [10, 20, 30], [60, 100, 120], 3)==180
  assert knapSack(30, [10, 20, 30], [60, 100, 120], 3)==160
  assert knapSack(20, [10, 20, 30], [60, 100, 120], 3)==100
output: def knapSack(W, wt, val, n):
      dp = [[0 for _ in range(W + 1)] for _ in range(n + 1)]
      
      for i in range(1, n + 1):
          for w in range(1, W + 1):
              if wt[i-1] <= w:
                  dp[i][w] = max(val[i-1] + dp[i-1][w-wt[i-1]], dp[i-1][w])
              else:
                  dp[i][w] = dp[i-1][w]
                  
      return dp[n][W]