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Preface
- FAQ
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Part I - Basics
- Basics Data Structure
- Basics Sorting
- Basics Algorithm
- Basics Misc
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Part II - Coding
- String
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Integer Array
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Remove Element
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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
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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
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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
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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
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Triangle
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Backpack
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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
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Jump Game
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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
Permutation Index II
Question
- lintcode: (198) Permutation Index II
Problem Statement
Given a permutation which may contain repeated numbers, find its index in all the permutations of these numbers, which are ordered in lexicographical order. The index begins at 1.
Example
Given the permutation [1, 4, 2, 2]
, return 3
.
题解
题 Permutation Index 的扩展,这里需要考虑重复元素,有无重复元素最大的区别在于原来的1!, 2!, 3!...
等需要除以重复元素个数的阶乘,颇有点高中排列组合题的味道。记录重复元素个数同样需要动态更新,引入哈希表这个万能的工具较为方便。
Python
class Solution:
# @param {int[]} A an integer array
# @return {long} a long integer
def permutationIndexII(self, A):
if A is None or len(A) == 0:
return 0
index = 1
factor = 1
for i in xrange(len(A) - 1, -1, -1):
hash_map = {A[i]: 1}
rank = 0
for j in xrange(i + 1, len(A)):
if A[j] in hash_map.keys():
hash_map[A[j]] += 1
else:
hash_map[A[j]] = 1
# get rank
if A[i] > A[j]:
rank += 1
index += rank * factor / self.dupPerm(hash_map)
factor *= (len(A) - i)
return index
def dupPerm(self, hash_map):
if hash_map is None or len(hash_map) == 0:
return 0
dup = 1
for val in hash_map.values():
dup *= self.factorial(val)
return dup
def factorial(self, n):
r = 1
for i in xrange(1, n + 1):
r *= i
return r
copy
C++
class Solution {
public:
/**
* @param A an integer array
* @return a long integer
*/
long long permutationIndexII(vector<int>& A) {
if (A.empty()) return 0;
long long index = 1;
long long factor = 1;
for (int i = A.size() - 1; i >= 0; --i) {
int rank = 0;
unordered_map<int, int> hash;
++hash[A[i]];
for (int j = i + 1; j < A.size(); ++j) {
++hash[A[j]];
if (A[i] > A[j]) {
++rank;
}
}
index += rank * factor / dupPerm(hash);
factor *= (A.size() - i);
}
return index;
}
private:
long long dupPerm(unordered_map<int, int> hash) {
if (hash.empty()) return 1;
long long dup = 1;
for (auto it = hash.begin(); it != hash.end(); ++it) {
dup *= fact(it->second);
}
return dup;
}
long long fact(int num) {
long long val = 1;
for (int i = 1; i <= num; ++i) {
val *= i;
}
return val;
}
};
copy
Java
public class Solution {
/**
* @param A an integer array
* @return a long integer
*/
public long permutationIndexII(int[] A) {
if (A == null || A.length == 0) return 0L;
Map<Integer, Integer> hashmap = new HashMap<Integer, Integer>();
long index = 1, fact = 1, multiFact = 1;
for (int i = A.length - 1; i >= 0; i--) {
// collect its repeated times and update multiFact
if (hashmap.containsKey(A[i])) {
hashmap.put(A[i], hashmap.get(A[i]) + 1);
multiFact *= hashmap.get(A[i]);
} else {
hashmap.put(A[i], 1);
}
// get rank every turns
int rank = 0;
for (int j = i + 1; j < A.length; j++) {
if (A[i] > A[j]) rank++;
}
// do divide by multiFact
index += rank * fact / multiFact;
fact *= (A.length - i);
}
return index;
}
}
copy
源码分析
在计算重复元素个数的阶乘时需要注意更新multiFact
的值即可,不必每次都去计算哈希表中的值。对元素A[i]
需要加入哈希表 - hash.put(A[i], 1);
,设想一下2, 2, 1, 1
的计算即可知。
复杂度分析
双重 for 循环,时间复杂度为 , 使用了哈希表,空间复杂度为 .