codeforces 337A

本文介绍了一个简单的算法问题,即如何为一群孩子从多种不同大小的拼图中选择出差异最小的一组作为礼物。通过排序和遍历的方法,找出连续n个拼图数量间的最小差距。

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The end of the school year is near and Ms. Manana, the teacher, will soon have to say goodbye to a yet another class. She decided to prepare a goodbye present for her n students and give each of them a jigsaw puzzle (which, as wikipedia states, is a tiling puzzle that requires the assembly of numerous small, often oddly shaped, interlocking and tessellating pieces).

The shop assistant told the teacher that there are m puzzles in the shop, but they might differ in difficulty and size. Specifically, the first jigsaw puzzle consists of f1 pieces, the second one consists of f2 pieces and so on.

Ms. Manana doesn't want to upset the children, so she decided that the difference between the numbers of pieces in her presents must be as small as possible. Let A be the number of pieces in the largest puzzle that the teacher buys and B be the number of pieces in the smallest such puzzle. She wants to choose such n puzzles that A - B is minimum possible. Help the teacher and find the least possible value of A - B.

Input

The first line contains space-separated integers n and m (2 ≤ n ≤ m ≤ 50). The second line contains m space-separated integersf1, f2, ..., fm (4 ≤ fi ≤ 1000) — the quantities of pieces in the puzzles sold in the shop.

Output

Print a single integer — the least possible difference the teacher can obtain.

Sample Input

Input
4 6
10 12 10 7 5 22
Output
5

Hint

Sample 1. The class has 4 students. The shop sells 6 puzzles. If Ms. Manana buys the first four puzzles consisting of 10, 12, 10 and 7 pieces correspondingly, then the difference between the sizes of the largest and the smallest puzzle will be equal to 5. It is impossible to obtain a smaller difference. Note that the teacher can also buy puzzles 1, 3, 4 and 5 to obtain the difference 5.


水题,排序,最大与最小的肯定是连续的n个

#include<iostream>
#include<cstdio>
#include<algorithm>
#include<cstring>
using namespace std;
const int INF=0x3f3f3f;
typedef long long LL;
int main()
{
	int n,m;
	int a[1050];
	while(~scanf("%d%d",&n,&m))
	{
		for(int i=1;i<=m;i++)
		{
			scanf("%d",&a[i]);
		}
		sort(a+1,a+m+1);
		int minv=INF;
		for(int i=1;i<=m-n+1;i++)
		{
			int sum=0;
			int maxn=-1;
			int minx=INF;
			for(int j=i;j<=n+i-1;j++)
			{
				minx=min(minx,a[j]);
				maxn=max(maxn,a[j]);
			}
			minv=min(minv,maxn-minx);
		}
		printf("%d\n",minv);
	}
	return 0;
}

内容概要:本文档详细介绍了一个基于MATLAB实现的跨尺度注意力机制(CSA)结合Transformer编码器的多变量时间序列预测项目。项目旨在精准捕捉多尺度时间序列特征,提升多变量时间序列的预测性能,降低模型计算复杂度与训练时间,增强模型的解释性和可视化能力。通过跨尺度注意力机制,模型可以同时捕获局部细节和全局趋势,显著提升预测精度和泛化能力。文档还探讨了项目面临的挑战,如多尺度特征融合、多变量复杂依赖关系、计算资源瓶颈等问,并提出了相应的解决方案。此外,项目模型架构包括跨尺度注意力机制模块、Transformer编码器层和输出预测层,文档最后提供了部分MATLAB代码示例。 适合人群:具备一定编程基础,尤其是熟悉MATLAB和深度学习的科研人员、工程师和研究生。 使用场景及目标:①需要处理多变量、多尺度时间序列数据的研究和应用场景,如金融市场分析、气象预测、工业设备监控、交通流量预测等;②希望深入了解跨尺度注意力机制和Transformer编码器在时间序列预测中的应用;③希望通过MATLAB实现高效的多变量时间序列预测模型,提升预测精度和模型解释性。 其他说明:此项目不仅提供了一种新的技术路径来处理复杂的时间序列数据,还推动了多领域多变量时间序列应用的创新。文档中的代码示例和详细的模型描述有助于读者快速理解和复现该项目,促进学术和技术交流。建议读者在实践中结合自己的数据集进行调试和优化,以达到最佳的预测效果。
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