# 3 Sum
### Source
- leetcode: [3Sum | LeetCode OJ](https://leetcode.com/problems/3sum/)
- lintcode: [(57) 3 Sum](http://www.lintcode.com/en/problem/3-sum/)
~~~
Given an array S of n integers, are there elements a, b, c in S such that a + b + c = 0?
Find all unique triplets in the array which gives the sum of zero.
Example
For example, given array S = {-1 0 1 2 -1 -4}, A solution set is:
(-1, 0, 1)
(-1, -1, 2)
Note
Elements in a triplet (a,b,c) must be in non-descending order. (ie, a ≤ b ≤ c)
The solution set must not contain duplicate triplets.
~~~
### 题解1 - 排序 + 哈希表 + 2 Sum
相比之前的 [2 Sum](http://algorithm.yuanbin.me/zh-cn/integer_array/2_sum.html), 3 Sum 又多加了一个数,按照之前 2 Sum 的分解为『1 Sum + 1 Sum』的思路,我们同样可以将 3 Sum 分解为『1 Sum + 2 Sum』的问题,具体就是首先对原数组排序,排序后选出第一个元素,随后在剩下的元素中使用 2 Sum 的解法。
### Python
~~~
class Solution:
"""
@param numbersbers : Give an array numbersbers of n integer
@return : Find all unique triplets in the array which gives the sum of zero.
"""
def threeSum(self, numbers):
triplets = []
length = len(numbers)
if length < 3:
return triplets
numbers.sort()
for i in xrange(length):
target = 0 - numbers[i]
# 2 Sum
hashmap = {}
for j in xrange(i + 1, length):
item_j = numbers[j]
if (target - item_j) in hashmap:
triplet = [numbers[i], target - item_j, item_j]
if triplet not in triplets:
triplets.append(triplet)
else:
hashmap[item_j] = j
return triplets
~~~
### 源码分析
1. 异常处理,对长度小于3的直接返回。
1. 排序输入数组,有助于提高效率和返回有序列表。
1. 循环遍历排序后数组,先取出一个元素,随后求得 2 Sum 中需要的目标数。
1. 由于本题中最后返回结果不能重复,在加入到最终返回值之前查重。
由于排序后的元素已经按照大小顺序排列,且在2 Sum 中先遍历的元素较小,所以无需对列表内元素再排序。
### 复杂度分析
排序时间复杂度 O(nlogn)O(n \log n)O(nlogn), 两重`for`循环,时间复杂度近似为 O(n2)O(n^2)O(n2),使用哈希表(字典)实现,空间复杂度为 O(n)O(n)O(n).
目前这段源码为比较简易的实现,leetcode 上的运行时间为500 + ms, 还有较大的优化空间,嗯,后续再进行优化。
### C++
~~~
class Solution {
public:
vector<vector<int> > threeSum(vector<int> &num)
{
vector<vector<int> > result;
if (num.size() < 3) return result;
int ans = 0;
sort(num.begin(), num.end());
for (int i = 0;i < num.size() - 2; ++i)
{
if (i > 0 && num[i] == num[i - 1])
continue;
int j = i + 1;
int k = num.size() - 1;
while (j < k)
{
ans = num[i] + num[j] + num[k];
if (ans == 0)
{
result.push_back({num[i], num[j], num[k]});
++j;
while (j < num.size() && num[j] == num[j - 1])
++j;
--k;
while (k >= 0 && num[k] == num[k + 1])
--k;
}
else if (ans > 0)
--k;
else
++j;
}
}
return result;
}
};
~~~
### 源码分析
同python解法不同,没有使用hash map
~~~
S = {-1 0 1 2 -1 -4}
排序后:
S = {-4 -1 -1 0 1 2}
↑ ↑ ↑
i j k
→ ←
i每轮只走一步,j和k根据S[i]+S[j]+S[k]=ans和0的关系进行移动,且j只向后走(即S[j]只增大),k只向前走(即S[k]只减小)
如果ans>0说明S[k]过大,k向前移;如果ans<0说明S[j]过小,j向后移;ans==0即为所求。
至于如何取到所有解,看代码即可理解,不再赘述。
~~~
### 复杂度分析
外循环i走了n轮,每轮j和k一共走n-i步,所以时间复杂度为O(n2)O(n^2)O(n2)。最终运行时间为52ms
### Reference
- [3Sum | 九章算法](http://www.jiuzhang.com/solutions/3sum/)
- [A simply Python version based on 2sum - O(n^2) - Leetcode Discuss](https://leetcode.com/discuss/32455/a-simply-python-version-based-on-2sum-o-n-2)
- 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
- Fast Power
- 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
- Convert Sorted List to Binary Search Tree
- Binary Search Tree Iterator
- Exhaustive Search
- Subsets
- Unique Subsets
- Permutations
- Unique Permutations
- Next Permutation
- Previous Permuation
- 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
- 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
- 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