# Fast Power
### Source
- lintcode: [(140) Fast Power](http://www.lintcode.com/en/problem/fast-power/)
### 题解
数学题,考察整数求模的一些特性,不知道这个特性的话此题一时半会解不出来,本题中利用的关键特性为:
~~~
(a * b) % p = ((a % p) * (b % p)) % p
~~~
即 a 与 b 的乘积模 p 的值等于 a, b 分别模 p 相乘后再模 p 的值,只能帮你到这儿了,不看以下的答案先想想知道此关系后如何解这道题。
首先不太可能先把 ana^nan 具体值求出来,太大了... 所以利用以上求模公式,可以改写 ana^nan 为:
an=an/2⋅an/2=an/4⋅an/4⋅an/4⋅an/4⋅=...a^n = a^{n/2} \cdot a^{n/2} = a^{n/4} \cdot a^{n/4} \cdot a^{n/4} \cdot a^{n/4} \cdot = ...an=an/2⋅an/2=an/4⋅an/4⋅an/4⋅an/4⋅=...
至此递归模型建立。
### Python
~~~
class Solution:
"""
@param a, b, n: 32bit integers
@return: An integer
"""
def fastPower(self, a, b, n):
if n == 1:
return a % b
elif n == 0:
# do not use `1` instead `1 % b` because `b = 1`
return 1 % b
elif n < 0:
return -1
# (a * b) % p = ((a % p) * (b % p)) % p
product = self.fastPower(a, b, n / 2)
product = (product * product) % b
if n % 2 == 1:
product = (product * a) % b
return product
~~~
### C++
~~~
class Solution {
public:
/*
* @param a, b, n: 32bit integers
* @return: An integer
*/
int fastPower(int a, int b, int n) {
if (1 == n) {
return a % b;
} else if (0 == n) {
// do not use 1 instead (1 % b)! b = 1
return 1 % b;
} else if (0 > n) {
return -1;
}
// (a * b) % p = ((a % p) * (b % p)) % p
// use long long to prevent overflow
long long product = fastPower(a, b, n / 2);
product = (product * product) % b;
if (1 == n % 2) {
product = (product * a) % b;
}
// cast long long to int
return (int) product;
}
};
~~~
### Java
~~~
class Solution {
/*
* @param a, b, n: 32bit integers
* @return: An integer
*/
public int fastPower(int a, int b, int n) {
if (n == 1) {
return a % b;
} else if (n == 0) {
return 1 % b;
} else if (n < 0) {
return -1;
}
// (a * b) % p = ((a % p) * (b % p)) % p
// use long to prevent overflow
long product = fastPower(a, b, n / 2);
product = (product * product) % b;
if (n % 2 == 1) {
product = (product * a) % b;
}
// cast long to int
return (int) product;
}
};
~~~
### 源码分析
分三种情况讨论 n 的值,需要特别注意的是`n == 0`,虽然此时 a0a^0a0 的值为1,但是不可直接返回1,因为`b == 1`时应该返回0,故稳妥的写法为返回`1 % b`.
递归模型中,需要注意的是要分 n 是奇数还是偶数,奇数的话需要多乘一个 a, 保存乘积值时需要使用`long`型防止溢出,最后返回时强制转换回`int`。
### 复杂度分析
使用了临时变量`product`,空间复杂度为 O(1)O(1)O(1), 递归层数约为 logn\log nlogn, 时间复杂度为 O(logn)O(\log n)O(logn), 栈空间复杂度也为 O(logn)O(\log n)O(logn).
### Reference
- [Lintcode: Fast Power 解题报告 - Yu's Garden - 博客园](http://www.cnblogs.com/yuzhangcmu/p/4174781.html)
- [Fast Power 参考程序 Java/C++/Python](http://www.jiuzhang.com/solutions/fast-power/)
- 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