- Preface
- FAQ
- Guidelines for Contributing
- Contributors
- Part I - Basics
- Basics Data Structure
- String
- Linked List
- Binary Tree
- Huffman Compression
- Queue
- Heap
- Stack
- Set
- Map
- Graph
- Basics Sorting
- 算法复习——排序
- 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
- Bitmap
- 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
- 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
- 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
- Permutation Index
- Permutation Index II
- Permutation Sequence
- Unique Binary Search Trees II
- 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
- Follow up
- 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
- 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
- APAC 2016 Round D
- Problem A. Dynamic Grid
- 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
- 術語表
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Factorial Trailing Zeroes
Source
- leetcode: Factorial Trailing Zeroes | LeetCode OJ
- lintcode: (2) Trailing Zeros
Write an algorithm which computes the number of trailing zeros in n factorial.
Example
11! = 39916800, so the out should be 2
Challenge
O(log N) time
题解 1 - Iterative
找阶乘数中末尾的连零数量,容易想到的是找相乘能为 10 的整数倍的数,如 2×52 \times 52×5, 1×101 \times 101×10 等,遥想当初做阿里笔试题时遇到过类似的题,当时想着算算 5 和 10 的个数就好了,可万万没想到啊,25 可以变为两个 5 相乘!真是蠢死了... 根据数论里面的知识,任何正整数都可以表示为它的质因数的乘积wikipedia 。所以比较准确的思路应该是计算质因数 5 和 2 的个数,取小的即可。质因数 2 的个数显然要大于 5 的个数,故只需要计算给定阶乘数中质因数中 5 的个数即可。原题的问题即转化为求阶乘数中质因数 5 的个数,首先可以试着分析下 100 以内的数,再试试 100 以上的数,聪明的你一定想到了可以使用求余求模等方法 :)
Python
class Solution:
# @param {integer} n
# @return {integer}
def trailingZeroes(self, n):
if n < 0:
return -1
count = 0
while n > 0:
n /= 5
count += n
return count
C++
class Solution {
public:
int trailingZeroes(int n) {
if (n < 0) {
return -1;
}
int count = 0;
for (; n > 0; n /= 5) {
count += (n / 5);
}
return count;
}
};
Java
public class Solution {
public int trailingZeroes(int n) {
if (n < 0) {
return -1;
}
int count = 0;
for (; n > 0; n /= 5) {
count += (n / 5);
}
return count;
}
}
源码分析
- 异常处理,小于 0 的数返回-1.
- 先计算 5 的正整数幂都有哪些,不断使用 n / 5 即可知质因数 5 的个数。
- 在循环时使用
n /= 5
而不是i *= 5
, 可有效防止溢出。
lintcode 和 leetcode 上的方法名不一样,在两个 OJ 上分别提交的时候稍微注意下。
复杂度分析
关键在于 n /= 5
执行的次数,时间复杂度 log5n\log_5 nlog5n,使用了 count
作为返回值,空间复杂度 O(1)O(1)O(1).
题解 2 - Recursive
可以使用迭代处理的程序往往用递归,而且往往更为优雅。递归的终止条件为 n <= 0
.
Python
class Solution:
# @param {integer} n
# @return {integer}
def trailingZeroes(self, n):
if n == 0:
return 0
elif n < 0:
return -1
else:
return n / 5 + self.trailingZeroes(n / 5)
C++
class Solution {
public:
int trailingZeroes(int n) {
if (n == 0) {
return 0;
} else if (n < 0) {
return -1;
} else {
return n / 5 + trailingZeroes(n / 5);
}
}
};
Java
public class Solution {
public int trailingZeroes(int n) {
if (n == 0) {
return 0;
} else if (n < 0) {
return -1;
} else {
return n / 5 + trailingZeroes(n / 5);
}
}
}
源码分析
这里将负数输入视为异常,返回-1 而不是 0. 注意使用递归时务必注意收敛和终止条件的返回值。这里递归层数最多不超过 log5n\log_5 nlog5n, 因此效率还是比较高的。
复杂度分析
递归层数最大为 log5n\log_5 nlog5n, 返回值均在栈上,可以认为没有使用辅助的堆空间。
Reference
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