input: Write a function to find the optimal solution for a 0-1 knapsack problem using dynamic programming. The function should take in the capacity of the knapsack, a list of item values, and a list of item weights, and return the maximum value that can be obtained. If the given lists are empty, return 0.
  
  assert knapSack(50, [60, 100, 120], [10, 20, 30], 3) == 220
  assert knapSack(30, [60, 100, 120], [10, 20, 30], 3) == 160
  assert knapSack(10, [60, 100, 120], [10, 20, 30], 3) == 60
  assert knapSack(0, [60, 100, 120], [10, 20, 30], 3) == 0
  assert knapSack(50, [], [], 0) == 0
output: def knapSack(capacity, values, weights):
      n = len(values)
      if n == 0 or capacity == 0:
          return 0
      
      dp = [[0] * (capacity + 1) for _ in range(n + 1)]
      
      for i in range(1, n + 1):
          for w in range(1, capacity + 1):
              if weights[i - 1] <= w:
                  dp[i][w] = max(dp[i - 1][w], values[i - 1] + dp[i - 1][w - weights[i - 1]])
              else:
                  dp[i][w] = dp[i - 1][w]
      
      return dp[n][capacity]