Say you have an array for which the ith element is the price of a given stock on day i. Design an algorithm to find the maximum profit. You may complete at most two transactions. Note: You may not engage in multiple transactions at the same time (ie, you
must sell the stock before you buy again). 这道题目可以采用两种方法来解,第一种解法是 分别从左往右扫描一次 以及从右往左扫描一次依次把得到的最大的maxpro 放到数组里面 stack1 stack2 最后求解的时候 将stack1[i]+stack2[i+1] 得到的最大值就是最大值。代码如下
class Solution: # @param prices, a list of integer # @return an integer def maxProfit(self, prices): if len(prices)==0: return 0 stack1=[] stack2=[] lowprice=prices[0] maxpro=0 highprice=prices[-1] for index in range(len(prices)): lowprice=min(lowprice,prices[index]) maxpro=max(maxpro,prices[index]-lowprice) stack1.append(maxpro) maxpro=0 for index in reversed(range(len(prices))): highprice=max(highprice,prices[index]) maxpro=max(maxpro,highprice-prices[index]) stack2.insert(0,maxpro) for index in range(len(prices)-1): maxpro=max(maxpro,stack1[index]+stack2[index+1]) return maxpro
第二种做法是用动态规划的方法解的
i表示第i天 j表示一共可以进行j次交易
全局最优的表达式为g[j]=max(g[j],l[j])
局部最优的表达为当前最优和 前一个全局最优加上当前可以挣的钱的最大 表达式为
l[j]=max(g[j-1]+max(dif,0),l[j]+dif)
具体代码如下:
class Solution: # @param prices, a list of integer # @return an integer def maxProfit(self, prices): g=[0,0,0] l=[0,0,0] for i in range(len(prices)-1): dif=prices[i+1]-prices[i] for j in reversed(range(1,3)): l[j]=max(g[j-1]+max(dif,0),l[j]+dif) g[j]=max(l[j],g[j]) return g[2]
时间: 2024-10-28 21:36:58