第一周下多重背包(Cash Machine POJ - 1276 )

A Bank plans to install a machine for cash withdrawal. The machine is able to deliver appropriate @ bills for a requested cash amount. The machine uses exactly N distinct bill denominations, say Dk, k=1,N, and for each denomination Dk the machine has a supply of nk bills. For example,

N=3, n1=10, D1=100, n2=4, D2=50, n3=5, D3=10

means the machine has a supply of 10 bills of @100 each, 4 bills of @50 each, and 5 bills of @10 each.

Call cash the requested amount of cash the machine should deliver and write a program that computes the maximum amount of cash less than or equal to cash that can be effectively delivered according to the available bill supply of the machine.

Notes:
@ is the symbol of the currency delivered by the machine. For instance, @ may stand for dollar, euro, pound etc.
Input
The program input is from standard input. Each data set in the input stands for a particular transaction and has the format:

cash N n1 D1 n2 D2 … nN DN

where 0 <= cash <= 100000 is the amount of cash requested, 0 <=N <= 10 is the number of bill denominations and 0 <= nk <= 1000 is the number of available bills for the Dk denomination, 1 <= Dk <= 1000, k=1,N. White spaces can occur freely between the numbers in the input. The input data are correct.
Output
For each set of data the program prints the result to the standard output on a separate line as shown in the examples below.
Sample Input

735 3  4 125  6 5  3 350
633 4  500 30  6 100  1 5  0 1
735 0
0 3  10 100  10 50  10 10

Sample Output

735
630
0
0

Hint
The first data set designates a transaction where the amount of cash requested is @735. The machine contains 3 bill denominations: 4 bills of @125, 6 bills of @5, and 3 bills of @350. The machine can deliver the exact amount of requested cash.

In the second case the bill supply of the machine does not fit the exact amount of cash requested. The maximum cash that can be delivered is @630. Notice that there can be several possibilities to combine the bills in the machine for matching the delivered cash.

In the third case the machine is empty and no cash is delivered. In the fourth case the amount of cash requested is @0 and, therefore, the machine delivers no cash.
题意:
你有n元钱,去银行取出来,银行里有只有固定数量的某些面值货币,用这些货币尽可能多的取出你的钱,输出取出的钱数。
先输入总钱数n,然后输入货币种类数m,随后输入m对数,表示货币数和货币面值

题解:
多种背包模板题
代码:

#include"stdio.h"
#include"string.h"
#include"algorithm"
using namespace std;
int m,n;
int dp[100010],cost[15],num[15];
void zeroonebag(int t)
{
	for(int i=n;i>=t;i--)//从n开始逆序更新
	dp[i]=max(dp[i],dp[i-t]+t);
} 
void completebag(int t)//t面值的货币储量大于总钱数
{
	for(int i=t;i<=n;i++)//更新t到总钱数n之间的dp的值,因为此时剩下的钱数可以选择此面值的货币
	dp[i]=max(dp[i],dp[i-t]+t);
}
void multibag()
{
	int k=1;
	int count=0;
	for(int i=1;i<=m;i++)//遍历每一个面值的货币
	{
		if(cost[i]>n)//如果单张此面值的货币就大于总钱数,则直接找下一面值
			continue;
		if(num[i]*cost[i]>=n)//此面值的货币储量大于总钱数
			completebag(cost[i]);
		else
		{
			k=1;
			count=num[i];
			while(k<count)
			{
				zeroonebag(k*cost[i]);
				count-=k;
				k*=2;//用二进制的形式进行优化
			}
			zeroonebag(count*cost[i]);//对二进制优化剩下的货币进行处理
		}
	}
}
int main()
{
	while(~scanf("%d",&n))
	{
		scanf("%d",&m);
		memset(num,0,sizeof(num));
		memset(cost,0,sizeof(cost));
		memset(dp,0,sizeof(dp));
		for(int i=1;i<=m;i++)
		scanf("%d %d",&num[i],&cost[i]);
		multibag();
		printf("%d\n",dp[n]);
	}
}

代码2
直接进行二进制优化,使题目变成01背包问题

#include"stdio.h"
#include"string.h"
#include"algorithm"
using namespace std;
int w[100010],dp[100010];
int main()
{
	int m,n;
	while(~scanf("%d %d",&n,&m))
	{
		memset(dp,0,sizeof(dp));
		int i,j,t,x,y,k=0;
		for(i=0;i<m;i++)
		{
			scanf("%d %d",&x,&y);
			t=1;
			while(x>=t)
			{
				w[k++]=y*t;
				x-=t;
				t*=2;
			}
			w[k++]=x*y;
		}
		for(i=0;i<k;i++)
		{
			for(j=n;j>=w[i];j--)
			dp[j]=max(dp[j-w[i]]+w[i],dp[j]);
		}
		printf("%d\n",dp[n]);
	}
	return 0;
}
基于遗传算法的微电网调度(风、光、蓄电池、微型燃气轮机)(Matlab代码实现)内容概要:本文档介绍了基于遗传算法的微电网调度模型,涵盖风能、太阳能、蓄电池和微型燃气轮机等多种能源形式,并通过Matlab代码实现系统优化调度。该模型旨在解决微电网中多能源协调运行的问题,优化能源分配,降低运行成本,提高可再生能源利用率,同时考虑系统稳定性与经济性。文中详细阐述了遗传算法在求解微电网多目标优化问题中的应用,包括编码方式、适应度函数设计、约束处理及算法流程,并提供了完整的仿真代码供复现与学习。此外,文档还列举了大量相关电力系统优化案例,如负荷预测、储能配置、潮流计算等,展示了广泛的应用背景和技术支撑。; 适合人群:具备一定电力系统基础知识和Matlab编程能力的研究生、科研人员及从事微电网、智能电网优化研究的工程技术人员。; 使用场景及目标:①学习遗传算法在微电网调度中的具体实现方法;②掌握多能源系统建模与优化调度的技术路线;③为科研项目、毕业设计或实际工程提供可复用的代码框架与算法参考; 阅读建议:建议结合Matlab代码逐段理解算法实现细节,重点关注目标函数构建与约束条件处理,同时可参考文档中提供的其他优化案例进行拓展学习,以提升综合应用能力。
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
红包 添加红包
表情包 插入表情
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值