Coins(背包DP)

从‘哒哒’声到静音顺滑:一文搞懂42步进电机细分控制的原理与实战(以TMC5160为例) 本文详细解析了42步进电机细分控制技术,以TMC5160为例,探讨如何通过正弦波电流调制和自适应波形整形实现静音运行。文章提供了实战配置指南,包括硬件连接、寄存器配置和调试技巧,帮助用户有效消除电机噪声,提升3D打印机、CNC机床等设备的运行效率。 阅读详情

Description
People in Silverland use coins.They have coins of value A1,A2,A3…An Silverland dollar.One day Tony opened his money-box and found there were some coins.He decided to buy a very nice watch in a nearby shop. He wanted to pay the exact price(without change) and he known the price would not more than m.But he didn’t know the exact price of the watch.
You are to write a program which reads n,m,A1,A2,A3…An and C1,C2,C3…Cn corresponding to the number of Tony’s coins of value A1,A2,A3…An then calculate how many prices(form 1 to m) Tony can pay use these coins.
Input
The input contains several test cases. The first line of each test case contains two integers n(1<=n<=100),m(m<=100000).The second line contains 2n integers, denoting A1,A2,A3…An,C1,C2,C3…Cn (1<=Ai<=100000,1<=Ci<=1000). The last test case is followed by two zeros.
Output
For each test case output the answer on a single line.
Sample Input
3 10
1 2 4 2 1 1
2 5
1 4 2 1
0 0
Sample Output
8
4

n种硬币,每种硬币ai价值,ci个,问能用其表示的数有多少个。

直接多重背包二进制优化,然后ttttt成一个傻逼,最后能手写的都手写,全部挂上手动挡终于卡了过去- -。

#include<cstdio>
#include<cstring>
#include<iostream>
#include<queue>
#include<vector>
#include<algorithm>
#include<string>
#include<cmath>
#include<set>
#include<map>
#include<vector>
#include<stack>
#include<utility>
#include<sstream>
using namespace std;
typedef long long ll;
const int inf = 0x3f3f3f3f;
const int maxn = 1005;
int f[100005],a[200],c[200];
int ans;
void Zeroonepack(int c,int v)
{
    for(int i = v;i >= c;i--)
    {
        if(f[i])continue;
        if(f[i - c])
        {
            f[i] = 1;ans++;
        }
    }
}
void Completepack(int c,int v)
{
    for(int i = c;i <= v;i++)
    {
        if(f[i])continue;
        if(f[i - c])
        {
            f[i] = 1;ans++;
        }
    }
}
void Multiplepack(int c,int num,int v)
{
    if(c*num >= v)
        Completepack(c,v);
    else
    {
        int k = 1;
        while(k < num)
        {
            Zeroonepack(k*c,v);
            num -= k;
            k <<= 1;
        }
        Zeroonepack(num*c,v);
    }
}
int main()
{
    #ifdef LOCAL
    freopen("C:\\Users\\ΡΡ\\Desktop\\in.txt","r",stdin);
    //freopen("C:\\Users\\ΡΡ\\Desktop\\out.txt","w",stdout);
    #endif // LOCAL
    int n,m;
    while(scanf("%d%d",&n,&m) != EOF)
    {
        if(!n&&!m)break;
        for(int i = 1;i <= n;i++)
            scanf("%d",&a[i]);
        for(int i = 1;i <= n;i++)
            scanf("%d",&c[i]);
        for(int i = 1;i <= m;i++)f[i] = 0;
        f[0] = 1;
        ans = 0;
        for(int i = 1;i <= n;i++)
            Multiplepack(a[i],c[i],m);
        printf("%d\n",ans);
    }
    return 0;
}

然而挑战上还有一种解法,dp[i][j]表示前i种硬币表示价值j之后剩下第i种硬币的个数,这种题目一般都是yes,no的bool型,用这种思路得到的信息更多。
若dp[i - 1][j]不是-1,说明前i - 1种硬币就可以表示j,那dp[i][j] = num[i],若dp[i][j - a[i]] <= 0或是j < a[i]那么dp[i][j]为-1。
否则dp[i][j] = dp[i][j - 1] - 1;
再观察表达式,发现第一维是可以省略的。

#include<cstdio>
#include<cstring>
#include<iostream>
#include<queue>
#include<vector>
#include<algorithm>
#include<string>
#include<cmath>
#include<set>
#include<map>
#include<vector>
#include<stack>
#include<utility>
#include<sstream>
using namespace std;
typedef long long ll;
const int inf = 0x3f3f3f3f;
const int maxn = 1005;
struct Node
{
    int a,c;
}node[105];
int dp[100005];
int main()
{
    #ifdef LOCAL
    freopen("C:\\Users\\ΡΡ\\Desktop\\in.txt","r",stdin);
    //freopen("C:\\Users\\ΡΡ\\Desktop\\out.txt","w",stdout);
    #endif // LOCAL
    int n,m;
    while(scanf("%d%d",&n,&m) != EOF)
    {
        if(!n&&!m)break;
        for(int i = 1;i <= n;i++)
            scanf("%d",&node[i].a);
        for(int i = 1;i <= n;i++)
            scanf("%d",&node[i].c);
        memset(dp,-1,sizeof(dp));
        dp[0] = 0;
        for(int i = 1;i <= n;i++)
        {
            for(int j = 0;j <= m;j++)
            {
                if(dp[j] >= 0)
                    dp[j] = node[i].c;
                else if(j < node[i].a || dp[j - node[i].a] <= 0)
                    dp[j] = -1;
                else
                    dp[j] = dp[j - node[i].a] - 1;

            }
        }
        int ans = 0;
        for(int i = 1;i <= m;i++)
            if(dp[i] >= 0)ans++;
        printf("%d\n",ans);
    }
    return 0;
}

2000ms过得,还算可以- -

IEC61850规约客户端软件开发实战(第六章) IEC 61850标准中的GOOSE(Generic Object Oriented Substation Event,面向通用对象的变电站事件)功能是变电站自动化系统的核心通信机制,主要用于实现智能电子设备(IED)间高可靠性、低延时的实时信号传输。本章简述其核心功能与技术特点。 阅读详情

相关推荐

车辆动力学仿真模型的可信度评估方法

以转向工况测试为例,实车采集的方向盘转角作为车辆动力学模型的输入,记录车辆动力学模型的横摆角速度的输出结果,计算横摆角速度的幅频特性,根据《GB/T 6323-2014 转向盘角脉冲试验》的方法,获取横摆角速度峰值、横摆角速度谐振峰频率、横摆角速度谐振峰水平,与采集的实车结果对比,并计算车辆动力学模型和实车对于关键对比参数的相对误差。由公式(2)计算可得,在制动工况和转向工况测试下,车辆动力学模型和实车的纵向车速、制动减速度、制动距离、横摆角速度基本一致,精度> 85%,具有较好的精度。

wangchen900705的博客 1958

poj1743硬币COINS三种解法(1个动态规划+2个搜索)

Description People in Silverland use coins.They have coins of value A1,A2,A3…An Silverland dollar.One day Tony opened his money-box and found there were some coins.He decided to buy a very nice watch in a nearby shop. He wanted to pay the exact price(witho

yxmbbd的博客 621

告别C盘爆红!手把手教你将WSL2下的Ubuntu 22.04完整迁移到D盘(附清理指南)

本文详细指导如何将WSL2下的Ubuntu 22.04从C盘无损迁移到D盘,包括导出备份、注销原系统、导入新位置等关键步骤,并提供迁移后的优化配置、开发环境衔接及长期维护技巧,帮助用户彻底解决C盘空间不足问题。

weixin_28698059的博客 472

【动态规划】背包问题相关题目

1.poj 1742 Description People in Silverland use coins.They have coins of value A1,A2,A3...An Silverland dollar.One day Tony opened his money-box and found there were some coins.He decided to bu...

weixin_30788731的博客 134

VS Code配置Java开发环境避坑指南:从JDK到Spring Boot插件全流程

本文详细介绍了在VS Code中配置Java开发环境的完整流程,涵盖JDK选择、环境变量配置、核心插件安装、构建工具优化及Spring Boot项目专项设置。特别针对常见问题提供避坑方案,帮助开发者快速搭建高效稳定的开发环境,提升Java和Spring Boot开发效率。

weixin_28839699的博客 979

2026最新 OpenClaw 手机端部署与实战:旧手机秒变 AI 智能终端(Android/iOS 双平台全覆盖)

OpenClaw 是2026年 GitHub 爆火的开源自托管 AI 执行网关,前身是 Clawdbot / Moltbot,主打“一句话让 AI 帮你完成真实操作”,核心能力包括发消息、整理文件、控制设备、自动化任务等,区别于普通对话式 AI,它能将 AI 指令转化为实际行动。无需高配置设备,无需专业技术,旧手机就能变身随身 AI 助手,既能实现自动化办公、生活便捷,又能保障数据隐私,还能体验极客玩法,这正是开源技术的魅力。# 初始化配置(按提示操作,配置 AI 模型 API Key)

wohaiweifaju的博客 5646

Coins (混合背包)

People in Silverland use coins.They have coins of value A1,A2,A3...An Silverland dollar.One day Tony opened his money-box and found there were some coins.He decided to buy a very nice wat...

FIVE 345

后摄模块辐射超标整改

某行车记录仪在EMI测试中出现辐射超标问题,通过模块剥离法定位到后摄模块的Ethernet数据线为噪声源。深入检查发现TYPE-C延长线屏蔽层未有效接地,形成辐射泄漏通道。整改措施包括加强屏蔽层360°接地、使用导电布改善连接处屏蔽等,最终使辐射降至EN55032 Class B限值以内。案例揭示了高速信号延长线中屏蔽连续性的重要性,建议设计阶段就重视连接器屏蔽设计和共模干扰抑制。

硬核科技 255

hdoj 2844 Coins【多重背包】【dp】

Coins Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others) Total Submission(s): 10300    Accepted Submission(s): 4106 Problem Description Whuacmers use coins

平平淡淡才是真 468

非技术面试题精选大全

回答提示:一般人回答这个问题过于平常,只说姓名、年龄、爱好、工作经验,这些在简历上都有,其实,企业最希望知道的是求职者能否胜任工作,包括:最强的技能、最深入研究的知识领域、个性中最积极的部分、做过的最成功的事,主要的成就等,这些都可以和学习无关,也可以和学习有关,但要突出积极的个性和做事的能力,说得合情合理企业才会相信。企业喜欢有礼貌的求职者。

吃面爱放香菜的博客 435

2024年11月架构设计师案例分析真题回顾,附参考答案、解析及所涉知识点(二)

软考高级系统架构设计师考试包含三个科目:信息系统综合知识、案例分析和系统架构设计论文。考试形式为机考。本文主要回顾2024年下半年(2024-11-10)系统架构设计师考试上午第二科案例分析的题目,同时附带参考解答、解析和所涉知识点。

一几文的编程学习笔记。 1660

HDOJ(HDU).2844 Coins (DP 多重背包+二进制优化)

HDOJ(HDU).2844 Coins (DP 多重背包+二进制优化)题意分析先把每种硬币按照二进制拆分好,然后做01背包即可。需要注意的是本题只需要求解可以凑出几种金钱的价格,而不需要输出种数。因此用0表示不可以,1表示可以。最后对dp数组扫描一遍即可。代码总览#include <iostream> #include <cstdio> #include <cstring> #include <a

Pengwill's Blog 480

win10 跳过U8环境检测的IE WebControls.rar

win10 安装u8的时候 提示IE WebControls 不通过,这个工具可以欺骗u8的检查。 跳过U8环境检测的IE WebControls。提示导入失败后,重新检测通过。

HDU2844 Coins(dp,背包,优化)

HDU2844 Coins(dp,背包,优化)

Pure_vv的博客 257

大中型超市收银系统(可直接运行)

光步前台收银系统系统功能:商品销售、商品退货、挂单、收银员权限、会员销售、单品取消、整单取消、商品查询、脱机销售、脱机数据上传,基本资料下载,开钱箱、小票打印、收款结算,支持多币种付款方式(现金、储值卡、礼券、微信等),支持多种促销。运行环境:.net 4.0登陆信息用户名:1001 密码:123456内置商品信息,商品编码可输入00001000 - 00010000,测试效果相关功能键F5 结算 F9挂单 F11商品查询 F8退货 Insert单品取消 Del整单取消 F12开钱箱F2 会员 Ctrl+Z退出系统 Home 锁屏 相关源码待公开

深度学习工具包 DeepLearningToolbox

深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

hdu 2844 coins DP多重背包

传送门:hdu 2844 coins题目大意给定商品种类n和背包容量m以及对应的价值/容量和数量,求组合成不大于背包容量m有多少种可能。解题思路这是一个多重背包的题目,价值等于重量。我们根据每种硬币的价值和数量进行判断是转换为完全背包还是01背包。当一种硬币的(数量*价值)大于背包的总容量的时候,也就是相当于在不大于容量m的情况下该种物品满足完全背包的定义所以对于这一种物品来说使用完全背包的思想来求

woshinannan的专栏 456

VC中sqlite的使用

在VC中对sqlite的使用,实现数据库大批数据的快速插入,读取数据库中的数据在ListCtrl中显示

POJ - 1742 Coins 背包dp变形

POJ 1742 首先多重背包有一种普通的二进制优化,然后这题还可以加一个判断如果,a[i] * c[i] &gt;= m 的话,那就和完全背包一样,不用多重背包。这样应该能过。 第二种做法,是参考了完全背包,完全背包可以用O(nm)的复杂度完成,是因为,遍历m的时候没有数量的限制。那对于这题多重背包,就要考虑在dp里面加上当前物品用掉的数量信息,从而达到完全背包的复杂度。所以用dp[i][j...

GeN的博客 307

POJ--1742 Coins(DP,多重背包)

POJ–1742 Coins 给定N种硬币,其中第 i 种硬币的面值为Ai,共有Ci个。 从中选出若干个硬币,把面值相加,若结果为S,则称“面值S能被拼成”。 求1~M之间能被拼成的面值有多少个。 输入格式 输入包含多组测试用例。 每组测试用例第一行包含两个整数N和M。 第二行包含2N个整数,分别表示A1,A2,…,AN和C1,C2,…,CN。 当输入用例N=0,M=0时,表示输入终止,且该用例无...

qq_43148759的博客 342

poj 1742 Coins dp 多重背包 优化

《挑战》练习题,楼天城男人八题。

ned_chu的博客 555

Coins(多重背包方案可行性dp + 优化)

Coins 题目 给出硬币面额及每种硬币的个数,求从1到m能凑出面额的个数。 思路 1.朴素的多重背包 题面给出的很明显的多重背包,定义dp为考虑前i种硬币,能凑出j元的方案可行性,可以得到第一版代码 O(nm^2) 代码 #include<iostream> #include<cstdio> #include<algorithm> #include<string> #include<cstring> #include<vector&

weixin_58110654的博客 558

【DP|多重背包可行性】POJ-1742 Coins

男人八题@楼天成之一——Coins。

JSure的代码库 1417

HDU 2844 Coins(DP 背包)

题目链接:http://acm.hdu.edu.cn/showproblem.php?pid=2844 求背包能装满的情况的条数,参见背包九讲,二进制优化完全背包和多重背包问题 #include #include #include #include using namespace std; const int maxn = 100100; int dp[maxn]; int n,m,v

sunrain的专栏 596

poj 1742 Coins 背包dp

Description People in Silverland use coins.They have coins of value A1,A2,A3…An Silverland dollar.One day Tony opened his money-box and found there were some coins.He decided to buy a very nice watch

liangzihao1的博客 220

Coins - HDU 2844 背包dp

Coins Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others) Total Submission(s): 6962    Accepted Submission(s): 2829 Problem Description Whuacmers use coins.

提比的强行AC 559

【DP】 POJ 3260 The Fewest Coins 背包

购买T 块钱的东西 交易过程zhon

Kewowlo 842

【DP|混合背包】POJ-3260 The Fewest Coins

The Fewest Coins Time Limit: 2000MS   Memory Limit: 65536K       Description Farmer John has gone to town to buy some farm supplies. Being a very efficient man, he always

JSure的代码库 1101

【POJ 1742】Coins【DP】【多重背包】

题目大意: 题目链接:http://poj.org/problem?id=1742 有nnn种面值不同的硬币,每种有c[i]c[i]c[i]个。求1到mmm有多少面值可以用这些硬币凑成? 思路: 很明显的完全背包。。。 前面WA,TLE,RE,CEWA,TLE,RE,CEWA,TLE,RE,CE全是用二进制拆分做的。。。后来实在没办法打了书上的方法。 设f[i]f[...

stoorz 446

(背包dp)UVA - 562 Dividing coins

题意:有n个硬币,每个硬币有个价值,两个人分配硬币,要求最公平分配时候两人拿到的钱的差。 分析:很明显,两人拿到的钱的差越小越公平。 一开始想,一定是一人一半最公平,所以直接把总和sum/2,对着half跑01背包,但是WA了,修改后分别讨论奇偶,额外进行一次sum-half的01背包,也WA,仔细想想觉得有些漏洞,可能有些东西还没考虑到。 干脆想直接跑完整的背包试试, 结果...

dijieluan7764的博客 157
上一篇: Cheapest Palindrome(DP)
下一篇: Ant Counting(DP)
Techay
博客等级 码龄11年 11粉丝 174原创
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
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
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
实付元
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