-
Preface
- FAQ
-
Part I - Basics
- Basics Data Structure
- Basics Sorting
- Basics Algorithm
- Basics Misc
-
Part II - Coding
- String
-
Integer Array
-
Remove Element
-
Zero Sum Subarray
-
Subarray Sum K
-
Subarray Sum Closest
-
Recover Rotated Sorted Array
-
Product of Array Exclude Itself
-
Partition Array
-
First Missing Positive
-
2 Sum
-
3 Sum
-
3 Sum Closest
-
Remove Duplicates from Sorted Array
-
Remove Duplicates from Sorted Array II
-
Merge Sorted Array
-
Merge Sorted Array II
-
Median
-
Partition Array by Odd and Even
-
Kth Largest Element
-
Remove Element
-
Binary Search
-
First Position of Target
-
Search Insert Position
-
Search for a Range
-
First Bad Version
-
Search a 2D Matrix
-
Search a 2D Matrix II
-
Find Peak Element
-
Search in Rotated Sorted Array
-
Search in Rotated Sorted Array II
-
Find Minimum in Rotated Sorted Array
-
Find Minimum in Rotated Sorted Array II
-
Median of two Sorted Arrays
-
Sqrt x
-
Wood Cut
-
First Position of Target
-
Math and Bit Manipulation
-
Single Number
-
Single Number II
-
Single Number III
-
O1 Check Power of 2
-
Convert Integer A to Integer B
-
Factorial Trailing Zeroes
-
Unique Binary Search Trees
-
Update Bits
-
Fast Power
-
Hash Function
-
Happy Number
-
Count 1 in Binary
-
Fibonacci
-
A plus B Problem
-
Print Numbers by Recursion
-
Majority Number
-
Majority Number II
-
Majority Number III
-
Digit Counts
-
Ugly Number
-
Plus One
-
Palindrome Number
-
Task Scheduler
-
Single Number
-
Linked List
-
Remove Duplicates from Sorted List
-
Remove Duplicates from Sorted List II
-
Remove Duplicates from Unsorted List
-
Partition List
-
Add Two Numbers
-
Two Lists Sum Advanced
-
Remove Nth Node From End of List
-
Linked List Cycle
-
Linked List Cycle II
-
Reverse Linked List
-
Reverse Linked List II
-
Merge Two Sorted Lists
-
Merge k Sorted Lists
-
Reorder List
-
Copy List with Random Pointer
-
Sort List
-
Insertion Sort List
-
Palindrome Linked List
-
LRU Cache
-
Rotate List
-
Swap Nodes in Pairs
-
Remove Linked List Elements
-
Remove Duplicates from Sorted List
-
Binary Tree
-
Binary Tree Preorder Traversal
-
Binary Tree Inorder Traversal
-
Binary Tree Postorder Traversal
-
Binary Tree Level Order Traversal
-
Binary Tree Level Order Traversal II
-
Maximum Depth of Binary Tree
-
Balanced Binary Tree
-
Binary Tree Maximum Path Sum
-
Lowest Common Ancestor
-
Invert Binary Tree
-
Diameter of a Binary Tree
-
Construct Binary Tree from Preorder and Inorder Traversal
-
Construct Binary Tree from Inorder and Postorder Traversal
-
Subtree
-
Binary Tree Zigzag Level Order Traversal
-
Binary Tree Serialization
-
Binary Tree Preorder Traversal
- Binary Search Tree
- Exhaustive Search
-
Dynamic Programming
-
Triangle
-
Backpack
-
Backpack II
-
Minimum Path Sum
-
Unique Paths
-
Unique Paths II
-
Climbing Stairs
-
Jump Game
-
Word Break
-
Longest Increasing Subsequence
-
Palindrome Partitioning II
-
Longest Common Subsequence
-
Edit Distance
-
Jump Game II
-
Best Time to Buy and Sell Stock
-
Best Time to Buy and Sell Stock II
-
Best Time to Buy and Sell Stock III
-
Best Time to Buy and Sell Stock IV
-
Distinct Subsequences
-
Interleaving String
-
Maximum Subarray
-
Maximum Subarray II
-
Longest Increasing Continuous subsequence
-
Longest Increasing Continuous subsequence II
-
Maximal Square
-
Triangle
- Graph
- Data Structure
- Big Data
- Problem Misc
-
Part III - Contest
- Google APAC
- Microsoft
- Appendix I Interview and Resume
-
Tags
Merge Sorted Array
Question
- leetcode: Merge Sorted Array | LeetCode OJ
- lintcode: (6) Merge Sorted Array
Given two sorted integer arrays A and B, merge B into A as one sorted array.
Example
A = [1, 2, 3, empty, empty], B = [4, 5]
After merge, A will be filled as [1, 2, 3, 4, 5]
Note
You may assume that A has enough space (size that is greater or equal to m + n)
to hold additional elements from B.
The number of elements initialized in A and B are m and n respectively.
copy
题解
因为本题有 in-place 的限制,故必须从数组末尾的两个元素开始比较;否则就会产生挪动,一旦挪动就会是 的。
自尾部向首部逐个比较两个数组内的元素,取较大的置于数组 A 中。由于 A 的容量较 B 大,故最后 m == 0
或者 n == 0
时仅需处理 B 中的元素,因为 A 中的元素已经在 A 中,无需处理。
Python
class Solution:
"""
@param A: sorted integer array A which has m elements,
but size of A is m+n
@param B: sorted integer array B which has n elements
@return: void
"""
def mergeSortedArray(self, A, m, B, n):
if B is None:
return A
index = m + n - 1
while m > 0 and n > 0:
if A[m - 1] > B[n - 1]:
A[index] = A[m - 1]
m -= 1
else:
A[index] = B[n - 1]
n -= 1
index -= 1
# B has elements left
while n > 0:
A[index] = B[n - 1]
n -= 1
index -= 1
copy
C++
class Solution {
public:
/**
* @param A: sorted integer array A which has m elements,
* but size of A is m+n
* @param B: sorted integer array B which has n elements
* @return: void
*/
void mergeSortedArray(int A[], int m, int B[], int n) {
int index = m + n - 1;
while (m > 0 && n > 0) {
if (A[m - 1] > B[n - 1]) {
A[index] = A[m - 1];
--m;
} else {
A[index] = B[n - 1];
--n;
}
--index;
}
// B has elements left
while (n > 0) {
A[index] = B[n - 1];
--n;
--index;
}
}
};
copy
Java
class Solution {
/**
* @param A: sorted integer array A which has m elements,
* but size of A is m+n
* @param B: sorted integer array B which has n elements
* @return: void
*/
public void mergeSortedArray(int[] A, int m, int[] B, int n) {
if (A == null || B == null) return;
int index = m + n - 1;
while (m > 0 && n > 0) {
if (A[m - 1] > B[n - 1]) {
A[index] = A[m - 1];
m--;
} else {
A[index] = B[n - 1];
n--;
}
index--;
}
// B has elements left
while (n > 0) {
A[index] = B[n - 1];
n--;
index--;
}
}
}
copy
源码分析
第一个 while 只能用条件与。
复杂度分析
最坏情况下需要遍历两个数组中所有元素,时间复杂度为 . 空间复杂度 .