There are two sorted arrays A and B of size m and n respectively. Find the median of the two sorted arrays. The overall run time complexity should be O(log (m+n)).
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a special case of find kth smallest
reference:
http://leetcode.com/2011/01/find-k-th-smallest-element-in-union-of.html
http://www.programcreek.com/2012/12/leetcode-median-of-two-sorted-arrays-java/
http://blog.csdn.net/yutianzuijin/article/details/11499917
un-solved:
#1 how to determine k's value: int kA = k * lenA / (lenB + lenA)
#2 why is this inclusive: endA = kA
class Solution { public: double findKth (int A[], int startA,int endA, int B[], int startB,int endB, int k) { int lenA = endA - startA + 1; int lenB = endB - startB + 1; if (lenA == 0) { return B[startB + k]; } if (lenB == 0) { return A[startA + k]; } if (k == 0) { return min(A[startA],B[startB]); } int kA = k * lenA / (lenB + lenA); // ??? int kB = k - kA - 1; kA += startA; kB += startB; if (A[kA] == B[kB]) { return A[kA]; } if (A[kA] > B[kB]) { k = k - (kB - startB + 1); endA = kA; // inclusive, why? startB = kB + 1; }else { k = k - (kA - startA + 1); startA = kA + 1; endB = kB; // inclusive } return findKth(A,startA,endA,B,startB,endB,k); } double findMedianSortedArrays(int A[], int m, int B[], int n) { if ((m + n) % 2 == 1) { return findKth(A,0,m - 1,B,0,n - 1, (m + n) / 2); }else { return (findKth(A,0,m - 1,B,0,n - 1, (m + n) / 2) + findKth(A,0,m - 1,B,0,n - 1, (m + n) / 2 - 1)) / 2.0; } } };Solution #2, reference: http://www2.myoops.org/course_material/mit/NR/rdonlyres/Electrical-Engineering-and-Computer-Science/6-046JFall-2005/30C68118-E436-4FE3-8C79-6BAFBB07D935/0/ps9sol.pdf
如果A[i]是中位数,则A[i]比A里i 个数都大, 比B里(m+n)/2 - i 个数都大
j = (m+n)/2 - i - 1
B[j] < A[i] < B[j + 1]
反之,A在B[j]和B[j + 1]的左侧或者右侧
class Solution { public: double findMedian (int A[],int B[],int m,int n,int left,int right) { if (left > right) { return findMedian(B,A,n,m,max(0,(m+n)/2 - m),min(n,(m+n)/2)); } int i = (left + right) / 2; int j = (m + n) / 2 - i - 1; if (j >= 0 && A[i] < B[j]) { return findMedian(A,B,m,n,i + 1, right); } if (j < n - 1 && A[i] > B[j + 1]) { return findMedian(A,B,m,n,left,i - 1); } if ((m + n) % 2 == 1) { return A[i]; } if (i > 0) { return (A[i] + max(B[j],A[i - 1])) / 2.0; } return (A[i] + B[j]) / 2.0; } double findMedianSortedArrays(int A[], int m, int B[], int n) { if (m > n) { return findMedian(A,B,m,n,max(0,(m+n)/2 - n),min(m,(m+n)/2)); } return findMedian(B,A,n,m,max(0,(m+n)/2 - m),min(n,(m+n)/2)); } };
Another one, takes half of k at each call, key us (k + 1) / 2
#1 当A里的元素不足 k / 2 个时,可以砍掉B的前 k / 2
#2 当 A[k/2] < B[k/2], 可以砍掉A的前 k / 2
以上都可以用反证法证明
k为0-based
- return B[BStart + k]意思为返回第 k + 1(1-based)小的elem
- AK = (k + 1) / 2, 为个数,所以 k 需要 + 1
- AStart + AK - 1, 同样道理,AK是个数(1-based),需要转换为 0-based
- return findKth(,....AStart + AK), AK是要被砍掉的个数,所以不用 - 1
base condition以下,k和AK,BK不可能为0,所以需要 - 1,不然AStart永远取不到
http://www.ninechapter.com/solutions/median-of-two-sorted-arrays/
class Solution { public: double findKth(int A[],int m, int AStart, int B[], int n, int BStart, int k) { if (AStart == m) return B[BStart + k]; if (BStart == n) return A[AStart + k]; if (k == 0) return min(A[AStart],B[BStart]); int AKey = INT_MAX; int BKey = INT_MAX; int AK = (k + 1) / 2; int BK = (k + 1) / 2; if (AStart + AK - 1 < m) { AKey = A[AStart + AK - 1]; } if (BStart + BK - 1 < n) { BKey = B[BStart + BK - 1]; } if (AKey < BKey) { return findKth(A,m,AStart + AK,B,n,BStart,k - AK); }else { return findKth(A,m,AStart,B,n,BStart + BK,k - BK); } } double findMedianSortedArrays(int A[], int m, int B[], int n) { int k = n + m; if (k % 2 == 0) { return (findKth(A,m, 0, B,n, 0, k / 2 - 1) + findKth(A,m,0, B, n, 0, k / 2)) / 2.0 ; } else { return findKth(A,m,0, B, n, 0, k / 2); } } };
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