- 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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Permutation Sequence
Source
- leetcode: Permutation Sequence | LeetCode OJ
- lintcode: (388) Permutation Sequence
Problem
Given n and k, return the k-th permutation sequence.
Example
For n = 3
, all permutations are listed as follows:
"123"
"132"
"213"
"231"
"312"
"321"
If k = 4
, the fourth permutation is "231"
Note
n will be between 1 and 9 inclusive.
Challenge
O(n*k) in time complexity is easy, can you do it in O(n^2) or less?
题解
和题 Permutation Index 正好相反,这里给定第几个排列的相对排名,输出排列值。和不同进制之间的转化类似,这里的『进制』为 1!, 2!...
, 以 n=3, k=4 为例,我们从高位到低位转化,直觉应该是用 k/(n-1)!
, 但以 n=3,k=5 和 n=3,k=6 代入计算后发现边界处理起来不太方便,故我们可以尝试将 k 减 1 进行运算,后面的基准也随之变化。第一个数可以通过 (k-1)/(n-1)!
进行计算,那么第二个数呢?联想不同进制数之间的转化,我们可以通过求模运算求得下一个数的 k-1
, 那么下一个数可通过 (k2 - 1)/(n-2)!
求得,这里不理解的可以通过进制转换类比进行理解。和减掉相应的阶乘值是等价的。
Python
class Solution:
"""
@param n: n
@param k: the k-th permutation
@return: a string, the k-th permutation
"""
def getPermutation(self, n, k):
# generate factorial list
factorial = [1]
for i in xrange(1, n + 1):
factorial.append(factorial[-1] * i)
nums = range(1, n + 1)
perm = []
for i in xrange(n):
rank = (k - 1) / factorial[n - i - 1]
k = (k - 1) % factorial[n - i - 1] + 1
# append and remove nums[rank]
perm.append(nums[rank])
nums.remove(nums[rank])
# combine digits
return "".join([str(digit) for digit in perm])
C++
class Solution {
public:
/**
* @param n: n
* @param k: the kth permutation
* @return: return the k-th permutation
*/
string getPermutation(int n, int k) {
// generate factorial list
vector<int> factorial = vector<int>(n + 1, 1);
for (int i = 1; i < n + 1; ++i) {
factorial[i] = factorial[i - 1] * i;
}
// generate digits ranging from 1 to n
vector<int> nums;
for (int i = 1; i < n + 1; ++i) {
nums.push_back(i);
}
vector<int> perm;
for (int i = 0; i < n; ++i) {
int rank = (k - 1) / factorial[n - i - 1];
k = (k - 1) % factorial[n - i - 1] + 1;
// append and remove nums[rank]
perm.push_back(nums[rank]);
nums.erase(std::remove(nums.begin(), nums.end(), nums[rank]), nums.end());
}
// transform a vector<int> to a string
std::stringstream result;
std::copy(perm.begin(), perm.end(), std::ostream_iterator<int>(result, ""));
return result.str();
}
};
Java
class Solution {
/**
* @param n: n
* @param k: the kth permutation
* @return: return the k-th permutation
*/
public String getPermutation(int n, int k) {
if (n <= 0 && k <= 0) return "";
int fact = 1;
// generate nums 1 to n
List<Integer> nums = new ArrayList<Integer>();
for (int i = 1; i <= n; i++) {
fact *= i;
nums.add(i);
}
// get the permutation digit
StringBuilder sb = new StringBuilder();
for (int i = n; i >= 1; i--) {
fact /= i;
// take care of rank and k
int rank = (k - 1) / fact;
k = (k - 1) % fact + 1;
// ajust the mapping of rank to num
sb.append(nums.get(rank));
nums.remove(rank);
}
return sb.toString();
}
}
源码分析
源码结构分为三步走,
- 建阶乘数组
- 生成排列数字数组
- 从高位到低位计算排列数值
复杂度分析
几个 for 循环,时间复杂度为 O(n)O(n)O(n), 用了与 n 等长的一些数组,空间复杂度为 O(n)O(n)O(n).
Reference
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