# Subarray Sum K
### Source
- GeeksforGeeks: [Find subarray with given sum - GeeksforGeeks](http://www.geeksforgeeks.org/find-subarray-with-given-sum/)
~~~
Given an nonnegative integer array, find a subarray where the sum of numbers is k.
Your code should return the index of the first number and the index of the last number.
Example
Given [1, 4, 20, 3, 10, 5], sum k = 33, return [2, 4].
~~~
### 题解1 - 哈希表
题 [Zero Sum Subarray | Data Structure and Algorithm](http://algorithm.yuanbin.me/zh-cn/integer_array/zero_sum_subarray.html) 的升级版,这道题求子串和为 K 的索引。首先我们可以考虑使用时间复杂度相对较低的哈希表解决。前一道题的核心约束条件为 f(i1)−f(i2)=0f(i_1) - f(i_2) = 0f(i1)−f(i2)=0,这道题则变为 f(i1)−f(i2)=kf(i_1) - f(i_2) = kf(i1)−f(i2)=k
### C++
~~~
#include <iostream>
#include <vector>
#include <map>
using namespace std;
class Solution {
public:
/**
* @param nums: A list of integers
* @return: A list of integers includes the index of the first number
* and the index of the last number
*/
vector<int> subarraySum(vector<int> nums, int k){
vector<int> result;
// curr_sum for the first item, index for the second item
// unordered_map<int, int> hash;
map<int, int> hash;
hash[0] = 0;
int curr_sum = 0;
for (int i = 0; i != nums.size(); ++i) {
curr_sum += nums[i];
if (hash.find(curr_sum - k) != hash.end()) {
result.push_back(hash[curr_sum - k]);
result.push_back(i);
return result;
} else {
hash[curr_sum] = i + 1;
}
}
return result;
}
};
int main(int argc, char *argv[])
{
int int_array1[] = {1, 4, 20, 3, 10, 5};
int int_array2[] = {1, 4, 0, 0, 3, 10, 5};
vector<int> vec_array1;
vector<int> vec_array2;
for (int i = 0; i != sizeof(int_array1) / sizeof(int); ++i) {
vec_array1.push_back(int_array1[i]);
}
for (int i = 0; i != sizeof(int_array2) / sizeof(int); ++i) {
vec_array2.push_back(int_array2[i]);
}
Solution solution;
vector<int> result1 = solution.subarraySum(vec_array1, 33);
vector<int> result2 = solution.subarraySum(vec_array2, 7);
cout << "result1 = [" << result1[0] << " ," << result1[1] << "]" << endl;
cout << "result2 = [" << result2[0] << " ," << result2[1] << "]" << endl;
return 0;
}
~~~
### 源码分析
与 Zero Sum Subarray 题的变化之处有两个地方,第一个是判断是否存在哈希表中时需要使用`hash.find(curr_sum - k)`, 最终返回结果使用`result.push_back(hash[curr_sum - k]);`而不是`result.push_back(hash[curr_sum]);`
### 复杂度分析
略,见 [Zero Sum Subarray | Data Structure and Algorithm](http://algorithm.yuanbin.me/zh-cn/integer_array/zero_sum_subarray.html)
### 题解2 - 利用单调函数特性
不知道细心的你是否发现这道题的隐含条件——**nonnegative integer array**, 这也就意味着子串和函数 f(i)f(i)f(i) 为「单调不减」函数。单调函数在数学中可是重点研究的对象,那么如何将这种单调性引入本题中呢?不妨设 i2>i1i_2 > i_1i2>i1, 题中的解等价于寻找 f(i2)−f(i1)=kf(i_2) - f(i_1) = kf(i2)−f(i1)=k, 则必有 f(i2)≥kf(i_2) \geq kf(i2)≥k.
我们首先来举个实际例子帮助分析,以整数数组 {1, 4, 20, 3, 10, 5} 为例,要求子串和为33的索引值。首先我们可以构建如下表所示的子串和 f(i)f(i)f(i).
| f(i)f(i)f(i) | 1 | 5 | 25 | 28 | 38 |
|-----|-----|-----|-----|-----|-----|
| iii | 0 | 1 | 2 | 3 | 4 |
要使部分子串和为33,则要求的第二个索引值必大于等于4,如果索引值再继续往后遍历,则所得的子串和必大于等于38,进而可以推断出索引0一定不是解。那现在怎么办咧?当然是把它扔掉啊!第一个索引值往后递推,直至小于33时又往后递推第二个索引值,于是乎这种技巧又可以认为是「两根指针」。
### C++
~~~
#include <iostream>
#include <vector>
#include <map>
using namespace std;
class Solution {
public:
/**
* @param nums: A list of integers
* @return: A list of integers includes the index of the first number
* and the index of the last number
*/
vector<int> subarraySum2(vector<int> &nums, int k){
vector<int> result;
int left_index = 0, curr_sum = 0;
for (int i = 0; i != nums.size(); ++i) {
while (curr_sum > k) {
curr_sum -= nums[left_index];
++left_index;
}
if (curr_sum == k) {
result.push_back(left_index);
result.push_back(i - 1);
return result;
}
curr_sum += nums[i];
}
return result;
}
};
int main(int argc, char *argv[])
{
int int_array1[] = {1, 4, 20, 3, 10, 5};
int int_array2[] = {1, 4, 0, 0, 3, 10, 5};
vector<int> vec_array1;
vector<int> vec_array2;
for (int i = 0; i != sizeof(int_array1) / sizeof(int); ++i) {
vec_array1.push_back(int_array1[i]);
}
for (int i = 0; i != sizeof(int_array2) / sizeof(int); ++i) {
vec_array2.push_back(int_array2[i]);
}
Solution solution;
vector<int> result1 = solution.subarraySum2(vec_array1, 33);
vector<int> result2 = solution.subarraySum2(vec_array2, 7);
cout << "result1 = [" << result1[0] << " ," << result1[1] << "]" << endl;
cout << "result2 = [" << result2[0] << " ," << result2[1] << "]" << endl;
return 0;
}
~~~
### 源码分析
使用`for`循环, 在`curr_sum > k`时使用`while`递减`curr_sum`, 同时递增左边索引`left_index`, 最后累加`curr_sum`。如果顺序不对就会出现 bug, 原因在于判断子串和是否满足条件时在递增之后(谢谢 @glbrtchen 汇报 bug)。
### 复杂度分析
看似有两重循环,由于仅遍历一次数组,且索引最多挪动和数组等长的次数。故最终时间复杂度近似为 O(2n)O(2n)O(2n), 空间复杂度为 O(1)O(1)O(1).
### Reference
- [Find subarray with given sum - GeeksforGeeks](http://www.geeksforgeeks.org/find-subarray-with-given-sum/)
- Preface
- Part I - Basics
- Basics Data Structure
- String
- Linked List
- Binary Tree
- Huffman Compression
- Queue
- Heap
- Stack
- Set
- Map
- Graph
- Basics Sorting
- Bubble Sort
- Selection Sort
- Insertion Sort
- Merge Sort
- Quick Sort
- Heap Sort
- Bucket Sort
- Counting Sort
- Radix Sort
- Basics Algorithm
- Divide and Conquer
- Binary Search
- Math
- Greatest Common Divisor
- Prime
- Knapsack
- Probability
- Shuffle
- Basics Misc
- Bit Manipulation
- Part II - Coding
- String
- strStr
- Two Strings Are Anagrams
- Compare Strings
- Anagrams
- Longest Common Substring
- Rotate String
- Reverse Words in a String
- Valid Palindrome
- Longest Palindromic Substring
- Space Replacement
- Wildcard Matching
- Length of Last Word
- Count and Say
- 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
- Binary Search
- Binary Search
- 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
- 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
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- Hash Function
- 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
- Linked List
- Remove Duplicates from Sorted List
- Remove Duplicates from Sorted List II
- Remove Duplicates from Unsorted List
- Partition List
- Two Lists Sum
- 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
- Check if a singly linked list is palindrome
- Delete Node in the Middle of Singly Linked List
- Rotate List
- Swap Nodes in Pairs
- Remove Linked List Elements
- 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 Search Tree
- Insert Node in a Binary Search Tree
- Validate Binary Search Tree
- Search Range in Binary Search Tree
- Convert Sorted Array to Binary Search Tree
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- Binary Search Tree Iterator
- Exhaustive Search
- Subsets
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- Permutations
- Unique Permutations
- Next Permutation
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- Unique Binary Search Trees II
- Permutation Index
- Permutation Index II
- Permutation Sequence
- Palindrome Partitioning
- Combinations
- Combination Sum
- Combination Sum II
- Minimum Depth of Binary Tree
- Word Search
- Dynamic Programming
- Triangle
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- 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
- Graph
- Find the Connected Component in the Undirected Graph
- Route Between Two Nodes in Graph
- Topological Sorting
- Word Ladder
- Bipartial Graph Part I
- Data Structure
- Implement Queue by Two Stacks
- Min Stack
- Sliding Window Maximum
- Longest Words
- Heapify
- Problem Misc
- Nuts and Bolts Problem
- String to Integer
- Insert Interval
- Merge Intervals
- Minimum Subarray
- Matrix Zigzag Traversal
- Valid Sudoku
- Add Binary
- Reverse Integer
- Gray Code
- Find the Missing Number
- Minimum Window Substring
- Continuous Subarray Sum
- Continuous Subarray Sum II
- Longest Consecutive Sequence
- Part III - Contest
- Google APAC
- APAC 2015 Round B
- Problem A. Password Attacker
- Microsoft
- Microsoft 2015 April
- Problem A. Magic Box
- Problem B. Professor Q's Software
- Problem C. Islands Travel
- Problem D. Recruitment
- Microsoft 2015 April 2
- Problem A. Lucky Substrings
- Problem B. Numeric Keypad
- Problem C. Spring Outing
- Microsoft 2015 September 2
- Problem A. Farthest Point
- Appendix I Interview and Resume
- Interview
- Resume