-
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
Factorial Trailing Zeroes
Question
- 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
copy
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 , 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
copy
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;
}
};
copy
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;
}
}
copy
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。
Warning 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 ,使用了count
作为返回值,空间复杂度 .
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)
copy
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);
}
}
};
copy
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);
}
}
}
copy
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 , 因此效率还是比较高的。
复杂度分析
递归层数最大为 , 返回值均在栈上,可以认为没有使用辅助的堆空间。