Best time to buy and sell stock IV¶
Time: O(KxN); Space: O(K); hard
Say you have an array for which the i-th element is the price of a given stock on day i.
Design an algorithm to find the maximum profit. You may complete at most k transactions.
Note:
You may not engage in multiple transactions at the same time (ie, you must sell the stock before you buy again).
Example 1:
Input: prices = [2,4,1], k = 2
Output: 2
Explanation:
Buy on day 1 (price = 2) and sell on day 2 (price = 4), profit = 4-2 = 2.
Example 2:
Input: prices = [3,2,6,5,0,3], k = 2
Output: 7
Explanation:
Buy on day 2 (price = 2) and sell on day 3 (price = 6), profit = 6-2 = 4.
Then buy on day 5 (price = 0) and sell on day 6 (price = 3), profit = 3-0 = 3.
Example 3:
Input: prices = [4, 4, 6, 1, 1, 4, 2 ,5], k = 2
Output: 6
Explanation:
Buy at 4 and sell at 6. Then buy at 1 and sell at 5. Your profit is 2 + 4 = 6.
Follow up:
O(KxN) time. N is the size of prices.
[6]:
class Solution1(object):
"""
Time: O(K*N)
Space: O(K)
"""
def maxProfit(self, prices, k):
"""
:type prices: List[int]
:type k: int
:rtype int
"""
if k >= len(prices) // 2:
return self.maxAtMostNPairsProfit(prices)
return self.maxAtMostKPairsProfit(prices, k)
def maxAtMostNPairsProfit(self, prices):
profit = 0
for i in range(len(prices) - 1):
profit += max(0, prices[i + 1] - prices[i])
return profit
def maxAtMostKPairsProfit(self, prices, k):
max_buy = [float("-inf") for _ in range(k + 1)]
max_sell = [0 for _ in range(k + 1)]
for i in range(len(prices)):
for j in range(1, min(k, i//2 + 1) + 1):
max_buy[j] = max(max_buy[j], max_sell[j-1] - prices[i])
max_sell[j] = max(max_sell[j], max_buy[j] + prices[i])
return max_sell[k]
[5]:
s = Solution1()
prices = [2,4,1]
k = 2
assert s.maxProfit(prices, k) == 2
prices = [3,2,6,5,0,3]
k = 2
assert s.maxProfit(prices, k) == 7
prices = [4, 4, 6, 1, 1, 4, 2 ,5]
k = 2
assert s.maxProfit(prices, k) == 6