# Nuts and Bolts Problem
### Source
- lintcode: [(399) Nuts & Bolts Problem](http://www.lintcode.com/en/problem/nuts-bolts-problem/)
~~~
Given a set of n nuts of different sizes and n bolts of different sizes.
There is a one-one mapping between nuts and bolts.
Comparison of a nut to another nut or a bolt to another bolt is not allowed.
It means nut can only be compared with bolt and bolt can only
be compared with nut to see which one is bigger/smaller.
We will give you a compare function to compare nut with bolt.
Example
Given nuts = ['ab','bc','dd','gg'], bolts = ['AB','GG', 'DD', 'BC'].
Your code should find the matching bolts and nuts.
one of the possible return:
nuts = ['ab','bc','dd','gg'], bolts = ['AB','BC','DD','GG'].
we will tell you the match compare function.
If we give you another compare function.
the possible return is the following:
nuts = ['ab','bc','dd','gg'], bolts = ['BC','AA','DD','GG'].
So you must use the compare function that we give to do the sorting.
The order of the nuts or bolts does not matter.
You just need to find the matching bolt for each nut.
~~~
### 题解
首先结合例子读懂题意,本题为 nuts 和 bolts 的配对问题,但是需要根据题目所提供的比较函数,且 nuts 与 nuts 之间的元素无法直接比较,compare 仅能在 nuts 与 bolts 之间进行。首先我们考虑若没有比较函数的限制,那么我们可以分别对 nuts 和 bolts 进行排序,由于是一一配对,故排完序后即完成配对。那么在只能通过比较对方元素得知相对大小时怎么完成排序呢?
我们容易通过以一组元素作为参考进行遍历获得两两相等的元素,这样一来在最坏情况下时间复杂度为 O(n2)O(n^2)O(n2), 相当于冒泡排序。根据排序算法理论可知基于比较的排序算法最好的时间复杂度为 O(nlogn)O(n \log n)O(nlogn), 也就是说这道题应该是可以进一步优化。回忆一些基于比较的排序算法,能达到 O(nlogn)O(n \log n)O(nlogn) 时间复杂度的有堆排、归并排序和快速排序,由于这里只能通过比较得到相对大小的关系,故可以联想到快速排序。
快速排序的核心即为定基准,划分区间。由于这里只能以对方的元素作为基准,故一趟划分区间后仅能得到某一方基准元素排序后的位置,那通过引入 O(n)O(n)O(n) 的额外空间来对已处理的基准元素进行标记如何呢?这种方法实现起来较为困难,因为只能对一方的元素划分区间,而对方的元素无法划分区间进而导致递归无法正常进行。
山穷水尽疑无路,柳暗花明又一村。由于只能通过对方进行比较,故需要相互配合进行 partition 操作(这个点确实难以想到)。核心在于:**首先使用 nuts 中的某一个元素作为基准对 bolts 进行 partition 操作,随后将 bolts 中得到的基准元素作为基准对 nuts 进行 partition 操作。**
### Python
~~~
# class Comparator:
# def cmp(self, a, b)
# You can use Compare.cmp(a, b) to compare nuts "a" and bolts "b",
# if "a" is bigger than "b", it will return 1, else if they are equal,
# it will return 0, else if "a" is smaller than "b", it will return -1.
# When "a" is not a nut or "b" is not a bolt, it will return 2, which is not valid.
class Solution:
# @param nuts: a list of integers
# @param bolts: a list of integers
# @param compare: a instance of Comparator
# @return: nothing
def sortNutsAndBolts(self, nuts, bolts, compare):
if nuts is None or bolts is None:
return
if len(nuts) != len(bolts):
return
self.qsort(nuts, bolts, 0, len(nuts) - 1, compare)
def qsort(self, nuts, bolts, l, u, compare):
if l >= u:
return
# find the partition index for nuts with bolts[l]
part_inx = self.partition(nuts, bolts[l], l, u, compare)
# partition bolts with nuts[part_inx]
self.partition(bolts, nuts[part_inx], l, u, compare)
# qsort recursively
self.qsort(nuts, bolts, l, part_inx - 1, compare)
self.qsort(nuts, bolts, part_inx + 1, u, compare)
def partition(self, alist, pivot, l, u, compare):
m = l
i = l + 1
while i <= u:
if compare.cmp(alist[i], pivot) == -1 or \
compare.cmp(pivot, alist[i]) == 1:
m += 1
alist[i], alist[m] = alist[m], alist[i]
i += 1
elif compare.cmp(alist[i], pivot) == 0 or \
compare.cmp(pivot, alist[i]) == 0:
# swap nuts[l]/bolts[l] with pivot
alist[i], alist[l] = alist[l], alist[i]
else:
i += 1
# move pivot to proper index
alist[l], alist[m] = alist[m], alist[l]
return m
~~~
### C++
~~~
/**
* class Comparator {
* public:
* int cmp(string a, string b);
* };
* You can use compare.cmp(a, b) to compare nuts "a" and bolts "b",
* if "a" is bigger than "b", it will return 1, else if they are equal,
* it will return 0, else if "a" is smaller than "b", it will return -1.
* When "a" is not a nut or "b" is not a bolt, it will return 2, which is not valid.
*/
class Solution {
public:
/**
* @param nuts: a vector of integers
* @param bolts: a vector of integers
* @param compare: a instance of Comparator
* @return: nothing
*/
void sortNutsAndBolts(vector<string> &nuts, vector<string> &bolts, Comparator compare) {
if (nuts.empty() || bolts.empty()) return;
if (nuts.size() != bolts.size()) return;
qsort(nuts, bolts, compare, 0, nuts.size() - 1);
}
private:
void qsort(vector<string>& nuts, vector<string>& bolts, Comparator compare,
int l, int u) {
if (l >= u) return;
// find the partition index for nuts with bolts[l]
int part_inx = partition(nuts, bolts[l], compare, l, u);
// partition bolts with nuts[part_inx]
partition(bolts, nuts[part_inx], compare, l, u);
// qsort recursively
qsort(nuts, bolts, compare, l, part_inx - 1);
qsort(nuts, bolts, compare, part_inx + 1, u);
}
int partition(vector<string>& str, string& pivot, Comparator compare,
int l, int u) {
int m = l;
for (int i = l + 1; i <= u; ++i) {
if (compare.cmp(str[i], pivot) == -1 ||
compare.cmp(pivot, str[i]) == 1) {
++m;
std::swap(str[m], str[i]);
} else if (compare.cmp(str[i], pivot) == 0 ||
compare.cmp(pivot, str[i]) == 0) {
// swap nuts[l]/bolts[l] with pivot
std::swap(str[i], str[l]);
--i;
}
}
// move pivot to proper index
std::swap(str[m], str[l]);
return m;
}
};
~~~
### Java
~~~
/**
* public class NBCompare {
* public int cmp(String a, String b);
* }
* You can use compare.cmp(a, b) to compare nuts "a" and bolts "b",
* if "a" is bigger than "b", it will return 1, else if they are equal,
* it will return 0, else if "a" is smaller than "b", it will return -1.
* When "a" is not a nut or "b" is not a bolt, it will return 2, which is not valid.
*/
public class Solution {
/**
* @param nuts: an array of integers
* @param bolts: an array of integers
* @param compare: a instance of Comparator
* @return: nothing
*/
public void sortNutsAndBolts(String[] nuts, String[] bolts, NBComparator compare) {
if (nuts == null || bolts == null) return;
if (nuts.length != bolts.length) return;
qsort(nuts, bolts, compare, 0, nuts.length - 1);
}
private void qsort(String[] nuts, String[] bolts, NBComparator compare,
int l, int u) {
if (l >= u) return;
// find the partition index for nuts with bolts[l]
int part_inx = partition(nuts, bolts[l], compare, l, u);
// partition bolts with nuts[part_inx]
partition(bolts, nuts[part_inx], compare, l, u);
// qsort recursively
qsort(nuts, bolts, compare, l, part_inx - 1);
qsort(nuts, bolts, compare, part_inx + 1, u);
}
private int partition(String[] str, String pivot, NBComparator compare,
int l, int u) {
//
int m = l;
for (int i = l + 1; i <= u; i++) {
if (compare.cmp(str[i], pivot) == -1 ||
compare.cmp(pivot, str[i]) == 1) {
//
m++;
swap(str, i, m);
} else if (compare.cmp(str[i], pivot) == 0 ||
compare.cmp(pivot, str[i]) == 0) {
// swap nuts[l]/bolts[l] with pivot
swap(str, i, l);
i--;
}
}
// move pivot to proper index
swap(str, m, l);
return m;
}
private void swap(String[] str, int l, int r) {
String temp = str[l];
str[l] = str[r];
str[r] = temp;
}
}
~~~
### 源码分析
难以理解的可能在`partition`部分,不仅需要使用`compare.cmp(alist[i], pivot)`, 同时也需要使用`compare.cmp(pivot, alist[i])`, 否则答案有误。第二个在于`alist[i] == pivot`时,需要首先将其和`alist[l]`交换,因为`i`是从`l+1`开始处理的,将`alist[l]`换过来后可继续和 pivot 进行比较。在 while 循环退出后在将当前遍历到的小于 pivot 的元素 alist[m] 和 alist[l] 交换,此时基准元素正确归位。对这一块不是很清楚的举个例子就明白了。
### 复杂度分析
快排的思路,时间复杂度为 O(2nlogn)O(2n \log n)O(2nlogn), 使用了一些临时变量,空间复杂度 O(1)O(1)O(1).
### Reference
- [LintCode/Nuts & Bolts Problem.py at master · algorhythms/LintCode](https://github.com/algorhythms/LintCode/blob/master/Nuts%20%26%20Bolts%20Problem.py)
- 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