-
Preface
- FAQ
-
Part I - Basics
- Basics Data Structure
- Basics Sorting
- Basics Algorithm
- Basics Misc
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Part II - Coding
- String
-
Integer Array
-
Remove Element
-
Zero Sum Subarray
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Subarray Sum K
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Subarray Sum Closest
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Recover Rotated Sorted Array
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Product of Array Exclude Itself
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Partition Array
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First Missing Positive
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2 Sum
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3 Sum
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3 Sum Closest
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Remove Duplicates from Sorted Array
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Remove Duplicates from Sorted Array II
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Merge Sorted Array
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Merge Sorted Array II
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Median
-
Partition Array by Odd and Even
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Kth Largest Element
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Remove Element
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Binary Search
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First Position of Target
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Search Insert Position
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Search for a Range
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First Bad Version
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Search a 2D Matrix
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Search a 2D Matrix II
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Find Peak Element
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Search in Rotated Sorted Array
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Search in Rotated Sorted Array II
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Find Minimum in Rotated Sorted Array
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Find Minimum in Rotated Sorted Array II
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Median of two Sorted Arrays
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Sqrt x
-
Wood Cut
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First Position of Target
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Math and Bit Manipulation
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Single Number
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Single Number II
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Single Number III
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O1 Check Power of 2
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Convert Integer A to Integer B
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Factorial Trailing Zeroes
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Unique Binary Search Trees
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Update Bits
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Fast Power
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Hash Function
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Happy Number
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Count 1 in Binary
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Fibonacci
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A plus B Problem
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Print Numbers by Recursion
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Majority Number
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Majority Number II
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Majority Number III
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Digit Counts
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Ugly Number
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Plus One
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Palindrome Number
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Task Scheduler
-
Single Number
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Linked List
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Remove Duplicates from Sorted List
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Remove Duplicates from Sorted List II
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Remove Duplicates from Unsorted List
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Partition List
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Add Two Numbers
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Two Lists Sum Advanced
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Remove Nth Node From End of List
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Linked List Cycle
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Linked List Cycle II
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Reverse Linked List
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Reverse Linked List II
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Merge Two Sorted Lists
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Merge k Sorted Lists
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Reorder List
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Copy List with Random Pointer
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Sort List
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Insertion Sort List
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Palindrome Linked List
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LRU Cache
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Rotate List
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Swap Nodes in Pairs
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Remove Linked List Elements
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Remove Duplicates from Sorted List
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Binary Tree
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Binary Tree Preorder Traversal
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Binary Tree Inorder Traversal
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Binary Tree Postorder Traversal
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Binary Tree Level Order Traversal
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Binary Tree Level Order Traversal II
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Maximum Depth of Binary Tree
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Balanced Binary Tree
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Binary Tree Maximum Path Sum
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Lowest Common Ancestor
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Invert Binary Tree
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Diameter of a Binary Tree
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Construct Binary Tree from Preorder and Inorder Traversal
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Construct Binary Tree from Inorder and Postorder Traversal
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Subtree
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Binary Tree Zigzag Level Order Traversal
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Binary Tree Serialization
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Binary Tree Preorder Traversal
- Binary Search Tree
- Exhaustive Search
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Dynamic Programming
-
Triangle
-
Backpack
-
Backpack II
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Minimum Path Sum
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Unique Paths
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Unique Paths II
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Climbing Stairs
-
Jump Game
-
Word Break
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Longest Increasing Subsequence
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Palindrome Partitioning II
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Longest Common Subsequence
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Edit Distance
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Jump Game II
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Best Time to Buy and Sell Stock
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Best Time to Buy and Sell Stock II
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Best Time to Buy and Sell Stock III
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Best Time to Buy and Sell Stock IV
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Distinct Subsequences
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Interleaving String
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Maximum Subarray
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Maximum Subarray II
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Longest Increasing Continuous subsequence
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Longest Increasing Continuous subsequence II
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Maximal Square
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Triangle
- Graph
- Data Structure
- Big Data
- Problem Misc
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Part III - Contest
- Google APAC
- Microsoft
- Appendix I Interview and Resume
-
Tags
Compare Strings
Tags: Basic Implementation, String, LintCode Copyright, Easy
Question
- lintcode: Compare Strings
Problem Statement
Compare two strings A and B, determine whether A contains all of the characters in B.
The characters in string A and B are all Upper Case letters.
Notice
The characters of B in A are not necessary continuous or ordered.
Example
For A = "ABCD"
, B = "ACD"
, return true
.
For A = "ABCD"
, B = "AABC"
, return false
.
题解
题 Two Strings Are Anagrams 的变形题。题目意思是问B中的所有字符是否都在A中,而不是单个字符。比如B="AABC"包含两个「A」,而A="ABCD"只包含一个「A」,故返回false. 做题时注意题意,必要时可向面试官确认。
既然不是类似 strstr 那样的匹配,直接使用两重循环就不太合适了。题目中另外给的条件则是A和B都是全大写单词,理解题意后容易想到的方案就是先遍历 A 和 B 统计各字符出现的频次,然后比较频次大小即可。嗯,祭出万能的哈希表。
Python
class Solution:
"""
@param A : A string includes Upper Case letters
@param B : A string includes Upper Case letters
@return : if string A contains all of the characters in B return True else return False
"""
def compareStrings(self, A, B):
letters = collections.defaultdict(int)
for a in A:
letters[a] += 1
for b in B:
letters[b] -= 1
if b not in letters or letters[b] < 0:
return False
return True
copy
C++
class Solution {
public:
/**
* @param A: A string includes Upper Case letters
* @param B: A string includes Upper Case letter
* @return: if string A contains all of the characters in B return true
* else return false
*/
bool compareStrings(string A, string B) {
if (A.size() < B.size()) return false;
const int UPPER_NUM = 26;
int letter_cnt[UPPER_NUM] = {0};
for (int i = 0; i != A.size(); ++i) {
++letter_cnt[A[i] - 'A'];
}
for (int i = 0; i != B.size(); ++i) {
--letter_cnt[B[i] - 'A'];
if (letter_cnt[B[i] - 'A'] < 0) return false;
}
return true;
}
};
copy
Java
public class Solution {
/**
* @param A : A string includes Upper Case letters
* @param B : A string includes Upper Case letter
* @return : if string A contains all of the characters in B return true else return false
*/
public boolean compareStrings(String A, String B) {
if (A == null || B == null) return false;
if (A.length() < B.length()) return false;
final int UPPER_NUM = 26;
int[] letter_cnt = new int[UPPER_NUM];
for (int i = 0; i < A.length(); i++) {
letter_cnt[A.charAt(i) - 'A']++;
}
for (int i = 0; i < B.length(); i++) {
letter_cnt[B.charAt(i) - 'A']--;
if (letter_cnt[B.charAt(i) - 'A'] < 0) return false;
}
return true;
}
}
copy
源码分析
Python 的dict
就是hash, 所以 Python 在处理需要用到 hash 的地方非常方便。collections 提供的数据结构非常实用,不过复杂度分析起来要麻烦些。
- 异常处理,B 的长度大于 A 时必定返回
false
, 包含了空串的特殊情况。 - 使用额外的辅助空间,统计各字符的频次。
复杂度分析
遍历一次 A 字符串,遍历一次 B 字符串,时间复杂度最坏 , 空间复杂度为 .