Kefa and Company

Kefa希望邀请朋友共进晚餐,但要确保没有人在金钱上感到自卑。本篇介绍了如何通过算法帮助Kefa选择最优的朋友组合,以最大化团队的整体友谊度。
L - Kefa and Company
Time Limit:2000MS     Memory Limit:262144KB     64bit IO Format:%I64d & %I64u

Description

Kefa wants to celebrate his first big salary by going to restaurant. However, he needs company.

Kefa has n friends, each friend will agree to go to the restaurant if Kefa asks. Each friend is characterized by the amount of money he has and the friendship factor in respect to Kefa. The parrot doesn't want any friend to feel poor compared to somebody else in the company (Kefa doesn't count). A friend feels poor if in the company there is someone who has at least d units of money more than he does. Also, Kefa wants the total friendship factor of the members of the company to be maximum. Help him invite an optimal company!

Input

The first line of the input contains two space-separated integers, n and d (1 ≤ n ≤ 105) — the number of Kefa's friends and the minimum difference between the amount of money in order to feel poor, respectively.

Next n lines contain the descriptions of Kefa's friends, the (i + 1)-th line contains the description of the i-th friend of type misi (0 ≤ mi, si ≤ 109) — the amount of money and the friendship factor, respectively.

Output

Print the maximum total friendship factir that can be reached.

Sample Input

Input
4 5
75 5
0 100
150 20
75 1
Output
100
Input
5 100
0 7
11 32
99 10
46 8
87 54
Output
111

Hint

In the first sample test the most profitable strategy is to form a company from only the second friend. At all other variants the total degree of friendship will be worse.

In the second sample test we can take all the friends.

AC代码:
#include<stdio.h>
#include<algorithm>
using namespace std;
struct node {
    int m,s;
} a[100200];
bool cmp(node A,node B) {
    return A.m<B.m;
}
int main() {
    int n,d;
    while(scanf("%d %d",&n,&d)!=EOF) {
        for(int i=0; i<n; i++) {
            scanf("%d %d",&a[i].m,&a[i].s);
        }
        sort(a,a+n,cmp);
        int l=0;
        __int64 max=0,sum=0;
        for(int i=0; i<n; i++) {
            sum+=(__int64)a[i].s;
            while(a[i].m>=a[l].m+d) {
                sum-=(__int64)a[l].s;
                l++;
            }
            if(max<sum)
                max=sum;
        }
        printf("%I64d\n",max);
    }
    return 0;
}

我现在要使用stata进行实证分析,实证模型如下gtfp1it=α+βkefa+γXit+μi+λt*ρp+ϵi,gtfp1为被解释变量,kefa为核心解释变量,政策冲击年份policy_year分别为2011年和2017年,代表第t年县级行政区i是否被纳入重点生态功能区,x为控制变量集,ϵ为随机干扰项;i表示市级行政区,t表示时间,p表示省级行政区,为提高因果推断的可靠性,本文控制了市级行政区固定效应id和省份—年份联合固定效应provinceid#year,将标准误聚类至市级行政区—年份层面,id_year,控制变量包括,1. 经济发展水平ed 2.产业结构is 3.创新能力creat 4.环境规制强度er 5.政府干预程度gover地6.财政压力pre 7.植被覆盖率plant 8.二氧化碳排放量co2,9,y ,现在我需要构建多时点双重差分模型,要求1.进行描述性统计并输出表格,2进行基准回归并输出表格,表格需要标注是否控制个体固定效应id和省份—年份联合固定效应provinceid#year 3进行创新cr中介机制分析并输出表格,进行环境规制er机制分析并输出表格 4,以2011和2017作为政策冲击时点进行多期did平行趋势检验,要求在进行均值计算后再进行平行趋势检验并输出表格 5,进行地区zone异质性分析,地区包括东中西,输出表格 6,进行安慰剂检验,要求根据kefa相关系数0.76输出结果图, 请确保所有命令完全契合我的模型和我的需求,要求命令必须真实可执行,需要调整的地方请做标注
最新发布
04-20
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