2014 ACM-ICPC Vietnam National Second Round

本文深入探讨了程序开发领域的核心内容,包括前端、后端、移动、游戏、大数据、开发工具等多个细分技术领域,提供了从理论到实践的全面指南。

B. Sum

time limit per test
2 seconds
memory limit per test
256 megabytes
input
standard input
output
standard output

Write a program to compute the following sum S given a positive integer n:

, where  is the largest integer not greater than x.

Input

The input file consists of several datasets. The first line of the input file contains the number of datasets which is a positive integer and is not greater than 30. The following lines describe the datasets.

Each dataset contains a positive integer n (n ≤ 1012) written on a separate line.

Output

For each dataset, write in one line the remainder of the computed sum S divided by 106.

Sample test(s)
input
2
1
5
output
1
10

思路:发现[x/i]的不同的值很少, 于是可以分段进行。

D. Treasure Box
time limit per test
2 seconds
memory limit per test
256 megabytes
input
standard input
output
standard output

Your team was exploring an ancient city. Suddenly you found an old scroll with 2 integer numbers N and K, which encrypts the secret code to open a treasure box. Considering a transformation on an integer X described as follows:

X = X + Xmod 100,

the secret code can be obtained by applying the above-described transformation K times successively to N.

Input

The input file consists of several datasets. The first line of the input file contains the number of datasets which is a positive integer and is not greater than 500.

Each dataset has two space-separated positive integers N and K (1 ≤ N ≤ 109, 1 ≤ K ≤ 109) written on a single line.

Output

For each dataset, write on a single line the secret number decrypted from N and K.

Sample test(s)
input
2
31102014 2
10101 10
output
31102056
10324

思路: X MOD 100 存在循环节。

 

E. ACM
time limit per test
9 seconds
memory limit per test
512 megabytes
input
standard input
output
standard output

An IT company puts out an advertisement to hire staff for a newly established office. They received n applications and organized an interview to select the best ones. They want to recruit candidates with both high level of expertise and good teamwork skill. Each candidate is assigned an ACM (Ability Coefficient of Multi-collectives) score that represents how the candidate meets the company’s internal selection criteria. Initially, everyone’s ACM score is 1.

Candidates are arranged into a round table of n seats, indexed from 1 to n. The first person sits next to the second person and the nthperson. For each interview question, candidates with indices from L and R form a group and register their collective answer to the system. If L ≤ R, the group consists of candidates at indices L, L + 1, L + 2, ..., R. If L > R, the group consists of candidates at indicesL, L + 1, ..., N, 1, ..., R. Depending on the answer, the ACM score of each group member is either multipled by X or divided by Y (in the later case, it is guaranteed that all ACM scores of the group are divisible by Y).

During the interview, the company may also request the system to output the product of the ACM scores of a group. This product could be a large number, so the system has to only output the value at modulo P. In summary, the system has to handle the following three types of queries:

  • L R P – compute the product of the ACM scores of all candidates from L to R, modulo P
  • L R X – the ACM score of each candidate from L to R is multiplied by X
  • L R Y – the ACM score of each candidate from L to R is divided by Y

For every query, we have 1 ≤ L, R ≤ N1 ≤ P ≤ 109 + 7, 1 ≤ X, Y ≤ 150.

Your task is to implement the system and output the computed products for every query of type 0.

Input

The input file consists of several datasets. The first line of the input file contains the number of datasets which is a positive integer and is not greater than 20. The following lines describe the datasets.

Each dataset comes in the following format:

  • The first line contains 2 integers n, m where n is the number of candidates and m is the number of queries to be processed(1 ≤ n, m ≤ 50000).
  • In the next m lines, the ith line contains the ith query.
Output

For each dataset, write out the corresponding outputs for queries of type 0 where each query output is on a separate line.

思路: 建立35课线段树,没课更新一个质因数的次幂。

#include <bits/stdc++.h>
#define LL long long

using namespace std;

struct node
{
    int l,r;
    LL sum,push;
};

const int X=150;
const int N=50010;

LL prime[35];
int pr;
node tree[35][N*4];

void init()
{
    pr=0;
    for (int i=2;i<=X;i++)
    {
        bool flag=true;
        for (int j=2;j<i;j++) if (i%j==0) flag=false;
        if (flag)
            prime[pr++]=i;
    }
}

void build(int idx, int t, int l, int r)
{
    tree[idx][t].l=l; tree[idx][t].r=r;
    tree[idx][t].sum=0; tree[idx][t].push=0;
    if (l==r) return;
    int mid=(l+r)/2;
    build(idx,t*2,l,mid);
    build(idx,t*2+1,mid+1,r);
}

void pushdown(int idx,int t)
{
    if (tree[idx][t].l!=tree[idx][t].r)
    {
        tree[idx][t*2].push+=tree[idx][t].push;
        tree[idx][t*2+1].push+=tree[idx][t].push;
        tree[idx][t*2].sum+=tree[idx][t].push*(tree[idx][t*2].r-tree[idx][t*2].l+1);
        tree[idx][t*2+1].sum+=tree[idx][t].push*(tree[idx][t*2+1].r-tree[idx][t*2+1].l+1);

        tree[idx][t].push=0;
    }
}

LL query(int idx, int t, int l, int r)
{
    if (l<=tree[idx][t].l&&tree[idx][t].r<=r)
    {
        return tree[idx][t].sum;
    }
    int mid=(tree[idx][t].l+tree[idx][t].r)/2;
    pushdown(idx,t);
    if (r<=mid)
        return query(idx,t*2,l,r);
    else
    if (l>mid)
        return query(idx,t*2+1,l,r);
    else
        return query(idx,t*2,l,mid)+query(idx,t*2+1,mid+1,r);
}

void update(int idx, int t, int l, int r, int x)
{
    if (l<=tree[idx][t].l&&tree[idx][t].r<=r)
    {
        tree[idx][t].push+=x;
        tree[idx][t].sum+=x*(tree[idx][t].r-tree[idx][t].l+1);
        return;
    }
    int mid=(tree[idx][t].l+tree[idx][t].r)/2;
    pushdown(idx,t);
    if (l<=mid)
        update(idx,t*2,l,r,x);
    if (r>mid)
        update(idx,t*2+1,l,r,x);

    tree[idx][t].sum=tree[idx][t*2].sum+tree[idx][t*2+1].sum;
}

LL multi(LL x, LL k,LL p)
{
    LL ans=1;
    x=x%p;
    while(k>0)
    {
        if(k%2==1) ans=(ans*x)%p;
        k=k/2;
        x=(x*x)%p;
    }
    return ans;
}

int main()
{
    int T,n,m,c,l,r;
    LL p;
    scanf("%d",&T);
    init();

    while(T--)
    {
        scanf("%d%d",&n,&m);
        for (int i=0;i<pr;i++) build(i,1,1,n);

        while(m--)
        {
            scanf("%d%d%d%I64d",&c,&l,&r,&p);
            if (c==0)
            {
                LL sum=1;
                for (int i=0;i<pr;i++)
                {
                    LL x=0;
                    if (l<=r) x=query(i,1,l,r); else x=query(i,1,l,n)+query(i,1,1,r);
                    sum=(sum*multi(prime[i],(LL)x,p))%p;
                }
                cout<<sum<<endl;
            }
            if (c==1)
            {
                for (int i=0;i<pr;i++) if (p%prime[i]==0)
                {
                    int x=0;
                    while(p%prime[i]==0) {x++; p=p/prime[i];}
                    if (l<=r) update(i,1,l,r,x); else {update(i,1,l,n,x); update(i,1,1,r,x);}
                }
            }
            if (c==2)
            {
                for (int i=0;i<pr;i++) if (p%prime[i]==0)
                {
                    int x=0;
                    while(p%prime[i]==0) {x++; p=p/prime[i];}
                    if (l<=r) update(i,1,l,r,-x); else {update(i,1,l,n,-x); update(i,1,1,r,-x);}
                }
            }
        }
    }
    return 0;
}

/*
2
6 5
0 1 5 1000000007
1 2 4 15
0 1 6 8704173
2 2 3 3
0 1 6 1000000007
6 6
1 1 4 20
1 2 6 15
0 1 6 9704331
2 3 6 5
0 1 4 1000000007
0 1 5 1000000007
*/

  

转载于:https://www.cnblogs.com/wzb-hust/p/4655627.html

复杂几何的多球近似MATLAB类及多球模型的比较 MATLAB类Approxi提供了一个框架,用于使用具有迭代缩放的聚集球体模型来近似解剖体积模型,以适应目标体积和模型比较。专为骨科、生物力学和计算几何应用而开发。 MATLAB class for multi-sphere approximation of complex geometries and comparison of multi-sphere models 主要特点: 球体模型生成 1.多球体模型生成:与Sihaeri的聚集球体算法的接口 2.音量缩放 基于体素的球体模型和参考几何体的交集。 迭代缩放球体模型以匹配目标体积。 3.模型比较:不同模型体素占用率的频率分析(多个评分指标) 4.几何分析:原始曲面模型和球体模型之间的顶点到最近邻距离映射(带颜色编码结果)。 如何使用: 1.代码结构:Approxi类可以集成到相应的主脚本中。代码的关键部分被提取到单独的函数中以供重用。 2.导入:将STL(或网格)导入MATLAB,并确保所需的函数,如DEM clusteredSphere(populateSpheres)和inpolyhedron,已添加到MATLAB路径中 3.生成多球体模型:使用DEM clusteredSphere方法从输入网格创建多球体模型 4.运行体积交点:计算多球体模型和参考几何体之间的基于体素的交点,并调整多球体模型以匹配目标体积 5.比较和可视化模型:比较多个多球体模型的体素频率,并计算多球体模型与原始表面模型之间的距离,以进行2D/3D可视化 使用案例: 骨科和生物力学体积建模 复杂结构的多球模型形状近似 基于体素拟合度量的模型选择 基于距离的患者特定几何形状和近似值分析 优点: 复杂几何的多球体模型 可扩展模型(基于体素)-自动调整到目标体积 可视化就绪输出(距离图)
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