codeforces 404B Marathon

本文介绍了一道编程题目,任务是在一个正方形跑道上模拟跑步路径,并根据特定条件计算运动员接收补给的具体位置。文章提供了完整的代码实现及解析。

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

Valera takes part in the Berland Marathon. The marathon race starts at the stadium that can be represented on the plane as a square whose lower left corner is located at point with coordinates (0, 0) and the length of the side equals a meters. The sides of the square are parallel to coordinate axes.

As the length of the marathon race is very long, Valera needs to have extra drink during the race. The coach gives Valera a bottle of drink each d meters of the path. We know that Valera starts at the point with coordinates (0, 0) and runs counter-clockwise. That is, when Valera covers a meters, he reaches the point with coordinates (a, 0). We also know that the length of the marathon race equals nd + 0.5 meters.

Help Valera's coach determine where he should be located to help Valera. Specifically, determine the coordinates of Valera's positions when he covers d, 2·d, ..., n·d meters.

Input

The first line contains two space-separated real numbers a and d (1 ≤ a, d ≤ 105), given with precision till 4 decimal digits after the decimal point. Number a denotes the length of the square's side that describes the stadium. Number d shows that after each d meters Valera gets an extra drink.

The second line contains integer n (1 ≤ n ≤ 105) showing that Valera needs an extra drink n times.

Output

Print n lines, each line should contain two real numbers xi and yi, separated by a space. Numbers xi and yi in the i-th line mean that Valera is at point with coordinates (xi, yi) after he covers i·d meters. Your solution will be considered correct if the absolute or relative error doesn't exceed 10 - 4.

Note, that this problem have huge amount of output data. Please, do not use cout stream for output in this problem.

Examples
input
2 5
2
output
1.0000000000 2.0000000000
2.0000000000 0.0000000000
input
4.147 2.8819
6
output
2.8819000000 0.0000000000
4.1470000000 1.6168000000
3.7953000000 4.1470000000
0.9134000000 4.1470000000
0.0000000000 2.1785000000
0.7034000000 0.0000000000

题意:在一个正方形边长为a的跑道跑步,起点位置在(0,0),逆时针跑,每距离d给一次水,一共给n次水,求每次给水的坐标


简单的模拟一下,注意一下浮点型取模就行,要用到fmod函数,用这个函数要math.h头文件,可以对浮点型进行取模运算,如果转化成整数再做运算会有误差,精度丢失较严重


#include<iostream>
#include<cstdio>
#include<algorithm>
#include<cstring>
#include<string>
#include<stack>
#include<queue>
#include<deque>
#include<set>
#include<map>
#include<cmath>
#include<vector>

using namespace std;

typedef long long ll;
typedef unsigned long long ull;
typedef pair<int, int> PII;

#define pi acos(-1.0)
#define eps 1e-15
#define pf printf
#define sf scanf
#define lson rt<<1,l,m
#define rson rt<<1|1,m+1,r
#define e tree[rt]
#define _s second
#define _f first
#define all(x) (x).begin,(x).end
#define mem(i,a) memset(i,a,sizeof i)
#define for0(i,a) for(int (i)=0;(i)<(a);(i)++)
#define for1(i,a) for(int (i)=1;(i)<=(a);(i)++)
#define mi ((l+r)>>1)
#define sqr(x) ((x)*(x))

const int inf=0x3f3f3f3f;
double a,d;
double ans[100005][2];
int n;
int A,D;

int main()
{
    while(~sf("%lf%lf",&a,&d))
    {
        double l=a*4.0;
        sf("%d",&n);
        for1(i,n)
        {
            double c=fmod(i*d,l);//浮点型取模
            int flag=1;
            while(c-a>0)//确定在哪一条边上
                c-=a,flag++;
            if(fabs(c-a)<eps)//eps要定义的足够小,不然就WA了
                c=0.0,flag++;
            if(flag==4)
                pf("%.10lf %.10lf\n",0.0,a-c);
            else if(flag==1)
                pf("%.10lf %.10lf\n",c,0.0);
            else if(flag==2)
                pf("%.10lf %.10lf\n",a,c);
            else
                pf("%.10lf %.10lf\n",a-c,a);
        }
    }
    return 0;
}

内容概要:本文档详细介绍了基于MATLAB实现多目标差分进化(MODE)算法进行无人机三维路径规划的项目实例。项目旨在提升无人机在复杂三维环境中路径规划的精度、实时性、多目标协调处理能力、障碍物避让能力和路径平滑性。通过引入多目标差分进化算法,项目解决了传统路径规划算法在动态环境和多目标优化中的不足,实现了路径长度、飞行安全距离、能耗等多个目标的协调优化。文档涵盖了环境建模、路径编码、多目标优化策略、障碍物检测与避让、路径平滑处理等关键技术模块,并提供了部分MATLAB代码示例。 适合人群:具备一定编程基础,对无人机路径规划和多目标优化算法感兴趣的科研人员、工程师和研究生。 使用场景及目标:①适用于无人机在军事侦察、环境监测、灾害救援、物流运输、城市管理等领域的三维路径规划;②通过多目标差分进化算法,优化路径长度、飞行安全距离、能耗等多目标,提升无人机任务执行效率和安全性;③解决动态环境变化、实时路径调整和复杂障碍物避让等问题。 其他说明:项目采用模块化设计,便于集成不同的优化目标和动态环境因素,支持后续算法升级与功能扩展。通过系统实现和仿真实验验证,项目不仅提升了理论研究的实用价值,还为无人机智能自主飞行提供了技术基础。文档提供了详细的代码示例,有助于读者深入理解和实践该项目。
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