Your are given an array of integers prices
, for which the i
-th element is the price of a given stock on day i
; and a non-negative integer fee
representing a transaction fee.
You may complete as many transactions as you like, but you need to pay the transaction fee for each transaction. You may not buy more than 1 share of a stock at a time (ie. you must sell the stock share before you buy again.)
Return the maximum profit you can make.
Example 1:
Input: prices = [1, 3, 2, 8, 4, 9], fee = 2 Output: 8 Explanation: The maximum profit can be achieved by:
Note:
0 < prices.length <= 50000
.0 < prices[i] < 50000
.0 <= fee < 50000
.Intuition and Algorithm
\nAt the end of the i
-th day, we maintain cash
, the maximum profit we could have if we did not have a share of stock, and hold
, the maximum profit we could have if we owned a share of stock.
To transition from the i
-th day to the i+1
-th day, we either sell our stock cash = max(cash, hold + prices[i] - fee)
or buy a stock hold = max(hold, cash - prices[i])
. At the end, we want to return cash
. We can transform cash
first without using temporary variables because selling and buying on the same day can\'t be better than just continuing to hold the stock.
Python
\nclass Solution(object):\n def maxProfit(self, prices, fee):\n cash, hold = 0, -prices[0]\n for i in range(1, len(prices)):\n cash = max(cash, hold + prices[i] - fee)\n hold = max(hold, cash - prices[i])\n return cash\n
Java
\nclass Solution {\n public int maxProfit(int[] prices, int fee) {\n int cash = 0, hold = -prices[0];\n for (int i = 1; i < prices.length; i++) {\n cash = Math.max(cash, hold + prices[i] - fee);\n hold = Math.max(hold, cash - prices[i]);\n }\n return cash;\n }\n}\n
Complexity Analysis
\nTime Complexity: , where is the number of prices.
\nSpace Complexity: , the space used by cash
and hold
.
Analysis written by: @awice.
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