题目链接:http://poj.org/problem?id=1180

Description

There is a sequence of N jobs to be processed on one machine. The jobs are numbered from 1 to N, so that the sequence is 1,2,..., N. The sequence of jobs must be partitioned into one or more batches, where each batch consists of consecutive jobs in the sequence. The processing starts at time 0. The batches are handled one by one starting from the first batch as follows. If a batch b contains jobs with smaller numbers than batch c, then batch b is handled before batch c. The jobs in a batch are processed successively on the machine. Immediately after all the jobs in a batch are processed, the machine outputs the results of all the jobs in that batch. The output time of a job j is the time when the batch containing j finishes.

A setup time S is needed to set up the machine for each batch. For each job i, we know its cost factor Fi and the time Ti required to process it. If a batch contains the jobs x, x+1,... , x+k, and starts at time t, then the output time of every job in that batch is t + S + (Tx + Tx+1 + ... + Tx+k). Note that the machine outputs the results of all jobs in a batch at the same time. If the output time of job i is Oi, its cost is Oi * Fi. For example, assume that there are 5 jobs, the setup time S = 1, (T1, T2, T3, T4, T5) = (1, 3, 4, 2, 1), and (F1, F2, F3, F4, F5) = (3, 2, 3, 3, 4). If the jobs are partitioned into three batches {1, 2}, {3}, {4, 5}, then the output times (O1, O2, O3, O4, O5) = (5, 5, 10, 14, 14) and the costs of the jobs are (15, 10, 30, 42, 56), respectively. The total cost for a partitioning is the sum of the costs of all jobs. The total cost for the example partitioning above is 153.

You are to write a program which, given the batch setup time and a sequence of jobs with their processing times and cost factors, computes the minimum possible total cost.

Input

Your program reads from standard input. The first line contains the number of jobs N, 1 <= N <= 10000. The second line contains the batch setup time S which is an integer, 0 <= S <= 50. The following N lines contain information about the jobs 1, 2,..., N in that order as follows. First on each of these lines is an integer Ti, 1 <= Ti <= 100, the processing time of the job. Following that, there is an integer Fi, 1 <= Fi <= 100, the cost factor of the job.

Output

Your program writes to standard output. The output contains one line, which contains one integer: the minimum possible total cost.

Sample Input

5
1
1 3
3 2
4 3
2 3
1 4

Sample Output

153

题意:

有N个工作排成一个序列,分别编号为1,2,3,…,N;

这些工作,被分成若干批("one or more"),并且满足:

  1. 每一批内的工作编号是连续的,机器处理“批(batchs)”的顺序按照序列的顺序来
  2. 处理一批所用时间为:预处理时间(setup time)S + 处理包内每个工作所耗时间之和
  3. 对于一个工作,它的完成时间O[i] = 开始处理它所在批的时刻t + S + 处理包内每个工作所耗时间之和
  4. 机器处理完一批,就立即同时输出该批内所有工作的结果

对于每个工作我们知道:

  1. 处理这项工作所耗时间T[i]
  2. 成本因子F[i](对于每项工作,它所要耗费的成本为O[i]*F[i])

现在要求,找到一个工作划分方案,使得成本耗费最少,输出该成本耗费。

题解:

设dp[i]代表从第i项工作到第N项工作需要耗费的最小成本;

设 $ {\rm{Tsum}}\left[ i \right] = \sum\limits_{k = i}^N {{\rm{T}}\left[ k \right]} {\rm{,}}\;\;{\rm{Fsum}}\left[ i \right] = \sum\limits_{k = i}^N {{\rm{F}}\left[ k \right]} $ ;

状态转移方程为:dp[i] = min{ dp[k] + ( S + Tsum[i] - Tsum[k] ) * Fsum[i] },i<k≤N

也就是说执行第k个batch的花费,看成不只包括第k个batch内所有工作的成本花费,同时还包括因执行第k个batch而延迟执行后续其他batch所增加的成本耗费。

那么对于计算dp[i]时中k可能选择的两个点a,b(i<a<b≤N),若有:

dp[b] + ( S + Tsum[i] - Tsum[b] ) * Fsum[i] ≤ dp[a] + ( S + Tsum[i] - Tsum[a] ) * Fsum[i]

则可以说b点优于a点;

对上式变形可得:

( dp[a] - dp[b] ) / ( Tsum[a] - Tsum[b] ) ≥ Fsum[i]

设g(a,b) = ( dp[a] - dp[b] ) / ( Tsum[a] - Tsum[b] ),则有

b点优于a点 <=> g(a,b) ≥ Fsum[i];

b点劣于a点 <=> g(a,b) < Fsum[i];

另外还有g(a,b) ≥ g(b,c),b必然被淘汰。

然后就可以进行斜率DP优化了(具体怎么优化参考之前的几篇文章HDU3507HDU2993HDU2829)。

AC代码:

#include<iostream>
#include<cstdio>
using namespace std;
const int maxn=+; int N,S;
int T[maxn],F[maxn];
int Tsum[maxn],Fsum[maxn];
int dp[maxn];
int q[maxn],head,tail; double g(int a,int b)
{
return double(dp[a]-dp[b])/double(Tsum[a]-Tsum[b]);
} int main()
{
scanf("%d%d",&N,&S);
for(int i=;i<=N;i++) scanf("%d%d",&T[i],&F[i]); Tsum[N+]=Fsum[N+]=;
for(int i=N;i>=;i--) Tsum[i]=Tsum[i+]+T[i], Fsum[i]=Fsum[i+]+F[i]; head=tail=;
q[tail++]=N+;
dp[N+]=;
for(int i=N,a,b;i>=;i--)
{
while(head+<tail)
{
b=q[head], a=q[head+];
if(g(a,b)<Fsum[i]) head++;
else break;
}
int k=q[head];
dp[i]=dp[k]+(S+Tsum[i]-Tsum[k])*Fsum[i]; while(head+<tail)
{
b=q[tail-], a=q[tail-];
if(g(a,b)>=g(b,i)) tail--;
else break;
}
q[tail++]=i;
} printf("%d\n",dp[]);
}

POJ 1180 - Batch Scheduling - [斜率DP]的更多相关文章

  1. poj 1180 Batch Scheduling (斜率优化)

    Batch Scheduling \(solution:\) 这应该是斜率优化中最经典的一道题目,虽然之前已经写过一道 \(catstransport\) 的题解了,但还是来回顾一下吧,这道题其实较那 ...

  2. POJ 1180 Batch Scheduling

    BTW: 刚在图书馆借了本算法艺术与信息学竞赛. 我多次有买这本书的冲动, 但每次在试看之后就放弃了, 倒不是因为书太难, 而是写的实在是太差. 大家对这本书的评价很高, 我觉得多是因为书的内容, 而 ...

  3. POJ 1180 Batch Scheduling(斜率优化DP)

    [题目链接] http://poj.org/problem?id=1180 [题目大意] N个任务排成一个序列在一台机器上等待完成(顺序不得改变), 这N个任务被分成若干批,每批包含相邻的若干任务. ...

  4. poj 1180:Batch Scheduling【斜率优化dp】

    我会斜率优化了!这篇讲的超级棒https://blog.csdn.net/shiyongyang/article/details/78299894?readlog 首先列个n方递推,设sf是f的前缀和 ...

  5. POJ 1180 Batch Scheduling (dp,双端队列)

    #include <iostream> using namespace std; + ; int S, N; int T[MAX_N], F[MAX_N]; int sum_F[MAX_N ...

  6. POJ 1260 Pearls (斜率DP)题解

    思路: 直接DP也能做,这里用斜率DP. dp[i] = min{ dp[j] + ( sum[i] - sum[j] + 10 )*pr[i]} ; k<j<i  =>  dp[j ...

  7. POJ1180 Batch Scheduling -斜率优化DP

    题解 将费用提前计算可以得到状态转移方程: $F_i = \min(F_j + sumT_i * (sumC_i - sumC_j) + S \times (sumC_N - sumC_j)$ 把方程 ...

  8. [kuangbin带你飞]专题二十 斜率DP

            ID Origin Title   20 / 60 Problem A HDU 3507 Print Article   13 / 19 Problem B HDU 2829 Lawr ...

  9. [POJ1180&POJ3709]Batch Scheduling&K-Anonymous Sequence 斜率优化DP

    POJ1180 Batch Scheduling Description There is a sequence of N jobs to be processed on one machine. T ...

随机推荐

  1. Dubbo -- 系统学习 笔记 -- 示例 -- 多版本

    Dubbo -- 系统学习 笔记 -- 目录 示例 想完整的运行起来,请参见:快速启动,这里只列出各种场景的配置方式 多版本 当一个接口实现,出现不兼容升级时,可以用版本号过渡,版本号不同的服务相互间 ...

  2. WPF依赖属性相关博客导航

    1.一站式WPF--依赖属性(DependencyProperty)一(什么是依赖属性,依赖属性的由来) 2.一站式WPF--依赖属性(DependencyProperty)二(涉及依赖属性的使用) ...

  3. PostgreSQL分布式架构之——PL/Proxy

    1. PL/Proxy的介绍 1.1 PL/Proxy概述 PL/Proxy是一款能在PostgreSQL数据库实现数据库水平拆分的软件:可以理解分布式架构(shared nothing);但是不是真 ...

  4. 使用 Beautiful Soup

    Beautiful Soup 用法: (1) 前面我们爬取一个网页,都是使用正则表达式来提取想要的信息,但是这种方式比较复杂,一旦有一个地方写错,就匹配不出来了,因此我们可以使用 Beautiful ...

  5. 3dmax导出模型使用相对路径读取纹理贴图

    Shift+T快捷键打开“资源跟踪”窗口

  6. [OSX] 使用 MacPorts 安装 Python 和 pip 指南

    Mac OS 未预装任何在 Unix/Linux 中常见的命令行包管理工具,Mac OS 中的 App Store 和自身的软件升级功能可以下载更新许多比较好的应用,但这些应用多数是满足普通消费者需求 ...

  7. 学习下新塘M0芯片的下载方法

    编程方式多种多样,解释这几种方式的原理,方便做后续的回答: 一.脱机 脱机的意思就是脱离PC机,有很多芯片必须连接PC才能烧录,比如某些FPGA芯片.MCU芯片.NAND Flash芯片等.脱机和在线 ...

  8. SQL Server 索引结构及其使用(一)[转]

    SQL Server 索引结构及其使用(一) 作者:freedk 一.深入浅出理解索引结构 实际上,您可以把索引理解为一种特殊的目录.微软的SQL SERVER提供了两种索引:聚集索引(cluster ...

  9. 【cs229-Lecture5】生成学习算法:1)高斯判别分析(GDA);2)朴素贝叶斯(NB)

    参考: cs229讲义 机器学习(一):生成学习算法Generative Learning algorithms:http://www.cnblogs.com/zjgtan/archive/2013/ ...

  10. IT零起步-CentOS6.4部署OpenVPN服务器

    OpenVPN是一个用于创建虚拟专用网络加密通道的软件包,实现二/三层的基于隧道的VPN.最早由James Yonan编写.OpenVPN允许创建的VPN使用公开密钥.数字证书.或者用户名/密码来进行 ...