the intervals

寻找最优区间算法
本文介绍了一个寻找最优区间的算法问题,对于给定的两个数组,需要找到包含特定元素的最小区间。通过排序和二分查找的方法实现了高效的解决方案。
The Intervals
Time Limit: 1 Second      Memory Limit: 32768 KB

Given two arrays of numbers {A(n)} and {B(m)}. For each B(i) in {B(m)}, find 2 numbers a and b from {A(n)}, such that B(i) is in [a,b) and b-a<=|b'-a'| for all a' and b' from {A(n)} such that [a',b') contains B(i).


Input

There are several test cases.

In each test case, the first line gives n and m.

The second line contains n numbers, which are the elements of {A(n)}.

The third line contains m nubmers, which are the elements of {B(m)}.


Output

For each B(i) in {B(m)}, output a line containing the interval [a,b).

If there is no such interval, output "no such interval" instead.

Print a blank line after each test case.


Sample Input

3 3
10 20 30
15 25 35


Sample Output

[10,20)
[20,30)
no such interval

 

/*题目要求找中间值,所以先排序比较好解,二分查找比顺序查找快,要灵活运用还要有创造力,把二分查找返回条件改写一下就能完成这道题了,若值不在最大或最小范围内则输出nosuch intervals,注意条件每个案例后面输出一个空行*/

 

 

 

#include<stdio.h>
#include<stdlib.h>
int a[1000],mid,low,high;
int comp(const void *a,const void *b)
{
          return *(int *)a-*(int *)b;
}
int search(int n,int k)
{
 low=0,high=n-1,mid;
 while(low<=high)
 {  
  mid=(low+high)/2;
  if(a[mid]<=k&&a[mid+1]>k) return(mid);
  if(a[mid]>k)
   high=mid-1;
  else low=mid+1;
 }
 return -1;
}

int main()
{   int b;
 register int i;
  int m,n;
 while(scanf("%d%d%*c",&m,&n)!=EOF)
 {  
  for(i=0;i<m;i++)
   scanf("%d",&a[i]);
  qsort(a,m,sizeof(int),comp);
     for(i=0;i<n;i++)
  {
   scanf("%d",&b);
   if(b<a[0]||b>=a[m-1]||search(m,b)==-1) {printf("no such interval/n");continue;}
   printf("[%d,%d)/n",a[mid],a[mid+1]);
  
  }
 printf("/n");
 }
 return 0;
}

翻译并用 latex 渲染: First, let's see how many zebra-Like numbers less than or equal to 1018 exist. It turns out there are only 30 of them, and based on some zebra-like number zi , the next one can be calculated using the formula zi+1=4⋅zi+1 . Then, we have to be able to quickly calculate the zebra value for an arbitrary number x . Since each subsequent zebra-like number is approximately 4 times larger than the previous one, intuitively, it seems like a greedy algorithm should be optimal: for any number x , we can determine its zebra value by subtracting the largest zebra-like number that does not exceed x , until x becomes 0 . Let's prove the correctness of the greedy algorithm: Assume that y is the smallest number for which the greedy algorithm does not work, meaning that in the optimal decomposition of y into zebra-like numbers, the largest zebra-like number zi that does not exceed y does not appear at all. If the greedy algorithm works for all numbers less than y , then in the decomposition of the number y , there must be at least one number zi−1 . And since y−zi−1 can be decomposed greedily and will contain at least 3 numbers zi−1 , we will end up with at least 4 numbers zi−1 in the decomposition. Moreover, there will be at least 5 numbers in the decomposition because 4⋅zi−1<zi , which means it is also less than y . Therefore, if the fifth number is 1 , we simply combine 4⋅zi−1 with 1 to obtain zi ; otherwise, we decompose the fifth number into 4 smaller numbers plus 1 , and we also combine this 1 with 4⋅zi−1 to get zi . Thus, the new decomposition of the number y into zebra-like numbers will have no more numbers than the old one, but it will include the number zi — the maximum zebra-like number that does not exceed y . This means that y can be decomposed greedily. We have reached a contradiction; therefore, the greedy algorithm works for any positive number. Now, let's express the greedy decomposition of the number x in a more convenient form. We will represent the decomposition as a string s of length 30 consisting of digits, where the i -th character will denote how many zebra numbers zi are present in this decomposition. Let's take a closer look at what such a string might look like: si∈{0,1,2,3,4} ; if si=4 , then for any j<i , the character sj=0 (this follows from the proof of the greedy algorithm). Moreover, any number generates a unique string of this form. This is very similar to representing a number in a new numeric system, which we will call zebroid. In summary, the problem has been reduced to counting the number of numbers in the interval [l,r] such that the sum of the digits in the zebroid numeral system equals x . This is a standard problem that can be solved using dynamic programming on digits. Instead of counting the suitable numbers in the interval [l,r] , we will count the suitable numbers in the intervals [1,l] and [1,r] and subtract the first from the second to get the answer. Let dp[ind][sum][num_less_m][was_4] be the number of numbers in the interval [1,m] such that: they have ind+1 digits; the sum of the digits equals sum ; num_less_m=0 if the prefix of ind+1 digits of the number m is lexicographically greater than these numbers, otherwise num_less_m=1 ; was_4=0 if there has not been a 4 in the ind+1 digits of these numbers yet, otherwise was_4=1 . Transitions in this dynamic programming are not very difficult — they are basically appending a new digit at the end. The complexity of the solution is O(log2A) , if we estimate the number of zebra-like numbers up to A=1018 as logA .
08-26
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