CodeForces - 408B

本文介绍了一个关于手工制作彩色彩灯的问题,旨在通过合理的纸张切割方式来最大化彩灯的总面积。文章提供了详细的解题思路及代码实现,帮助读者理解如何在限定条件下求解最优解。

B. Garland
time limit per test
1 second
memory limit per test
256 megabytes
input
standard input
output
standard output

Once little Vasya read an article in a magazine on how to make beautiful handmade garland from colored paper. Vasya immediately went to the store and bought n colored sheets of paper, the area of each sheet is 1 square meter.

The garland must consist of exactly m pieces of colored paper of arbitrary area, each piece should be of a certain color. To make the garland, Vasya can arbitrarily cut his existing colored sheets into pieces. Vasya is not obliged to use all the sheets to make the garland.

Vasya wants the garland to be as attractive as possible, so he wants to maximize the total area of ​​m pieces of paper in the garland. Calculate what the maximum total area of ​​the pieces of paper in the garland Vasya can get.

Input

The first line contains a non-empty sequence of n (1 ≤ n ≤ 1000) small English letters ("a"..."z"). Each letter means that Vasya has a sheet of paper of the corresponding color.

The second line contains a non-empty sequence of m (1 ≤ m ≤ 1000) small English letters that correspond to the colors of the pieces of paper in the garland that Vasya wants to make.

Output

Print an integer that is the maximum possible total area of the pieces of paper in the garland Vasya wants to get or -1, if it is impossible to make the garland from the sheets he's got. It is guaranteed that the answer is always an integer.

Examples
input
aaabbac
aabbccac
output
6
input
a
z
output
-1
解题思路:
统计所有的字母个数,分三种情况:
1.如果字符串s2有而字符串s1没有,直接输出-1
2.如果字符串s2中字母个数对应s1中的字母个数大则取s1中字母个数
3.如果字符串s2中字母个数对应s1中的字母个数小则取s2中的字母个数

AC代码:

#include<cstdio>
#include<cstring>
#include<algorithm>

using namespace std;

int main(){
	char s[1010],s1[1010];
	int a[30],a1[30];
	int len,len1;
	
	scanf("%s%s",s,s1);
		len=strlen(s);
		len1=strlen(s1);
		memset(a, 0, sizeof(a));  
        memset(a1, 0, sizeof(a1));  
		for(int i=0;i<len;i++){
			a[s[i]-'a']++;
		}		
		for(int i=0;i<len1;i++){
			a1[s1[i]-'a']++;
		}
		int sum=0,flag=1;
		for(int i=0;i<26;i++){
			if(a1[i]!=0&&a[i]==0){
				printf("-1\n");
				flag=0;
				break;
			}	
			if(a1[i]<=a[i]){
				sum+=a1[i];
			}
			else{
				sum+=a[i];
			}			
		}
		if(flag){
			printf("%d\n",sum);
		}
	
	return 0;
}



同步定位与地图构建(SLAM)技术为移动机器人或自主载具在未知空间中的导航提供了核心支撑。借助该技术,机器人能够在探索过程中实时构建环境地图并确定自身位置。典型的SLAM流程涵盖传感器数据采集、数据处理、状态估计及地图生成等环节,其核心挑战在于有效处理定位与环境建模中的各类不确定性。 Matlab作为工程计算与数据可视化领域广泛应用的数学软件,具备丰富的内置函数与专用工具箱,尤其适用于算法开发与仿真验证。在SLAM研究方面,Matlab可用于模拟传感器输出、实现定位建图算法,并进行系统性能评估。其仿真环境能显著降低实验成本,加速算法开发与验证周期。 本次“SLAM-基于Matlab的同步定位与建图仿真实践项目”通过Matlab平台完整再现了SLAM的关键流程,包括数据采集、滤波估计、特征提取、数据关联与地图更新等核心模块。该项目不仅呈现了SLAM技术的实际应用场景,更为机器人导航与自主移动领域的研究人员提供了系统的实践参考。 项目涉及的核心技术要点主要包括:传感器模型(如激光雷达与视觉传感器)的建立与应用、特征匹配与数据关联方法、滤波器设计(如扩展卡尔曼滤波与粒子滤波)、图优化框架(如GTSAM与Ceres Solver)以及路径规划与避障策略。通过项目实践,参与者可深入掌握SLAM算法的实现原理,并提升相关算法的设计与调试能力。 该项目同时注重理论向工程实践的转化,为机器人技术领域的学习者提供了宝贵的实操经验。Matlab仿真环境将复杂的技术问题可视化与可操作化,显著降低了学习门槛,提升了学习效率与质量。 实践过程中,学习者将直面SLAM技术在实际应用中遇到的典型问题,包括传感器误差补偿、动态环境下的建图定位挑战以及计算资源优化等。这些问题的解决对推动SLAM技术的产业化应用具有重要价值。 SLAM技术在工业自动化、服务机器人、自动驾驶及无人机等领域的应用前景广阔。掌握该项技术不仅有助于提升个人专业能力,也为相关行业的技术发展提供了重要支撑。随着技术进步与应用场景的持续拓展,SLAM技术的重要性将日益凸显。 本实践项目作为综合性学习资源,为机器人技术领域的专业人员提供了深入研习SLAM技术的实践平台。通过Matlab这一高效工具,参与者能够直观理解SLAM的实现过程,掌握关键算法,并将理论知识系统应用于实际工程问题的解决之中。 资源来源于网络分享,仅用于学习交流使用,请勿用于商业,如有侵权请联系我删除!
### Codeforces Problem 976C Solution in Python For solving problem 976C on Codeforces using Python, efficiency becomes a critical factor due to strict time limits aimed at distinguishing between efficient and less efficient solutions[^1]. Given these constraints, it is advisable to focus on optimizing algorithms and choosing appropriate data structures. The provided code snippet offers insight into handling string manipulation problems efficiently by customizing comparison logic for sorting elements based on specific criteria[^2]. However, for addressing problem 976C specifically, which involves determining the winner ('A' or 'B') based on frequency counts within given inputs, one can adapt similar principles of optimization but tailored towards counting occurrences directly as shown below: ```python from collections import Counter def determine_winner(): for _ in range(int(input())): count_map = Counter(input().strip()) result = "A" if count_map['A'] > count_map['B'] else "B" print(result) determine_winner() ``` This approach leverages `Counter` from Python’s built-in `collections` module to quickly tally up instances of 'A' versus 'B'. By iterating over multiple test cases through a loop defined by user input, this method ensures that comparisons are made accurately while maintaining performance standards required under tight computational resources[^3]. To further enhance execution speed when working with Python, consider submitting codes via platforms like PyPy instead of traditional interpreters whenever possible since they offer better runtime efficiencies especially important during competitive programming contests where milliseconds matter significantly.
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