Description
Find the contiguous subarray within an array (containing at least one number) which has the largest sum.
For example, given the array [-2,1,-3,4,-1,2,1,-5,4],
the contiguous subarray [4,-1,2,1] has the largest sum = 6.
设计
这是一道Divide and Conquer类型的题目。使用分治法的话,可以将序列对半分为两个子序列,递归地在子序列中求最大连续和;求出子序列的最大连续和后,从序列的分界点分别向左和向右求出最大连续和并相加,将结果与子序列返回的最大连续和比较,即可得出解。
这种算法的复杂度递归方程是T(n)=2T(n/2)+O(n),根据大师定理,T(n)=O(nlogn)。
题目提示有O(n)复杂度的算法。考虑先求出从序列首元素到后面每个元素的连续和,用数组储存起来。然后遍历该数组,记录当前遇到的最小的连续和,并计算数组当前下标的连续和减去遇到的最小连续和,大于之前的最大和的话,就替换最大和。
class Solution {
public:
int maxSubArray(vector<int>& nums) {
initializeAnArrayForSavingContiguousSums(nums);
calculateSumsOfContiguousSubarraysFromZeroToEveryIndex(nums);
int maxSum = findMaxSumOfSubArrays();
deleteTheArraySavingContiguousSums();
return maxSum;
}
private:
int* contiguousSums;
int size;
void initializeAnArrayForSavingContiguousSums(vector<int>& nums) {
size = nums.size();
contiguousSums = new int[size];
}
void calculateSumsOfContiguousSubarraysFromZeroToEveryIndex(vector<int>& nums) {
contiguousSums[0] = nums[0];
for (int i = 1; i < size; i++)
contiguousSums[i] = contiguousSums[i - 1] + nums[i];
}
int findMaxSumOfSubArrays() {
int currentMinContiguousSum = 0;
int maxSum = contiguousSums[0];
for (int i = 0; i < size; i++) {
if (maxSum < contiguousSums[i] - currentMinContiguousSum)
maxSum = contiguousSums[i] - currentMinContiguousSum;
if (currentMinContiguousSum > contiguousSums[i])
currentMinContiguousSum = contiguousSums[i];
}
return maxSum;
}
void deleteTheArraySavingContiguousSums() {
delete []contiguousSums;
size = 0;
}
};
分析
算法复杂度为O(n)。但是这种算法有点复杂,不够Leetcode该题讨论里的算法好。引用第2个解答:
this problem was discussed by Jon Bentley (Sep. 1984 Vol. 27 No. 9 Communications of the ACM P885)
the paragraph below was copied from his paper (with a little modifications)
algorithm that operates on arrays: it starts at the left end (element A[1]) and scans through to the right end (element A[n]), keeping track of the maximum sum subvector seen so far. The maximum is initially A[0]. Suppose we’ve solved the problem for A[1 … i - 1]; how can we extend that to A[1 … i]? The maximum
sum in the first I elements is either the maximum sum in the first i - 1 elements (which we’ll call MaxSoFar), or it is that of a subvector that ends in position i (which we’ll call MaxEndingHere).
MaxEndingHere is either A[i] plus the previous MaxEndingHere, or just A[i], whichever is larger.
public static int maxSubArray(int[] A) {
int maxSoFar=A[0], maxEndingHere=A[0];
for (int i=1;i<A.length;++i){
maxEndingHere= Math.max(maxEndingHere+A[i],A[i]);
maxSoFar=Math.max(maxSoFar, maxEndingHere);
}
return maxSoFar;
}
第3和第4个解答也不错,不过我感觉还是这个解答精简干练,而且很好理解。