Technocup 2017 - Elimination Round 2 C. Road to Cinema —— 二分

【Web服务器运维】基于Nginx与Keepalived的高可用负载均衡系统部署:实现故障自动切换与虚拟IP管理 内容概要:本文详细介绍了如何通过Nginx与Keepalived实现高可用的负载均衡架构。首先讲解了Nginx的安装部署流程,包括依赖环境的准备、源码解压编译、启动及配置重载;接着介绍了Keepalived的安装步骤及其服务管理方式;重点在于通过配置Keepalived的主备节点(MASTER/BACKUP),结合VRRP协议和虚拟IP(VIP)实现故障自动切换,并通过自定义脚本nginx_check.sh实时检测Nginx运行状态,确保服务高可用;最后补充了在云环境中因组播限制导致心跳不稳定时,应改用单播(unicast)方式进行通信的解决方案。; 适合人群:具备Linux系统操作基础、熟悉网络基础知识,从事运维、DevOps或Web服务部署的技术人员,尤其是工作1-3年的工程师。; 使用场景及目标:①搭建高可用的Web负载均衡系统,避免单点故障;②掌握Nginx与Keepalived协同工作的原理与实战配置方法;③应用于生产环境中提升服务稳定性与容灾能力。; 阅读建议:学习时应结合实验环境动手操作,重点关注keepalived.conf配置差异与nginx_check.sh脚本逻辑,理解VRRP机制及单播替代组播的实际应用场景,注意根据实际网络环境调整IP与网卡参数。 立即下载

题目链接:http://codeforces.com/problemset/problem/729/C


C. Road to Cinema
time limit per test
1 second
memory limit per test
256 megabytes
input
standard input
output
standard output

Vasya is currently at a car rental service, and he wants to reach cinema. The film he has bought a ticket for starts in t minutes. There is a straight road of length s from the service to the cinema. Let's introduce a coordinate system so that the car rental service is at the point 0, and the cinema is at the point s.

There are k gas stations along the road, and at each of them you can fill a car with any amount of fuel for free! Consider that this operation doesn't take any time, i.e. is carried out instantly.

There are n cars in the rental service, i-th of them is characterized with two integers ci and vi — the price of this car rent and the capacity of its fuel tank in liters. It's not allowed to fuel a car with more fuel than its tank capacity vi. All cars are completely fueled at the car rental service.

Each of the cars can be driven in one of two speed modes: normal or accelerated. In the normal mode a car covers 1 kilometer in 2minutes, and consumes 1 liter of fuel. In the accelerated mode a car covers 1 kilometer in 1 minutes, but consumes 2 liters of fuel. The driving mode can be changed at any moment and any number of times.

Your task is to choose a car with minimum price such that Vasya can reach the cinema before the show starts, i.e. not later than in tminutes. Assume that all cars are completely fueled initially.

Input

The first line contains four positive integers n, k, s and t (1 ≤ n ≤ 2·105, 1 ≤ k ≤ 2·105, 2 ≤ s ≤ 109, 1 ≤ t ≤ 2·109) — the number of cars at the car rental service, the number of gas stations along the road, the length of the road and the time in which the film starts.

Each of the next n lines contains two positive integers ci and vi (1 ≤ ci, vi ≤ 109) — the price of the i-th car and its fuel tank capacity.

The next line contains k distinct integers g1, g2, ..., gk (1 ≤ gi ≤ s - 1) — the positions of the gas stations on the road in arbitrary order.

Output

Print the minimum rent price of an appropriate car, i.e. such car that Vasya will be able to reach the cinema before the film starts (not later than in t minutes). If there is no appropriate car, print -1.

Examples
input
3 1 8 10
10 8
5 7
11 9
3
output
10
input
2 2 10 18
10 4
20 6
5 3
output
20
Note

In the first sample, Vasya can reach the cinema in time using the first or the third cars, but it would be cheaper to choose the first one. Its price is equal to 10, and the capacity of its fuel tank is 8. Then Vasya can drive to the first gas station in the accelerated mode in 3minutes, spending 6 liters of fuel. After that he can full the tank and cover 2 kilometers in the normal mode in 4 minutes, spending 2liters of fuel. Finally, he drives in the accelerated mode covering the remaining 3 kilometers in 3 minutes and spending 6 liters of fuel.




题解:

1.由于题目没说明车越贵,容量越大,所以需要将价格贵但容量小的车丢弃。

做法是:先按车的容量升序排列,然后用单调队列处理,使得队列中的车的价格也递增。

2.二分车的下标。



学习之处:

单调队列:

int N = 0;
for(int i = 1; i<=n; i++)
{
    while(N>=1 && a[i]<=a[N]) N--;  // a[i]<a[N] 还是 a[i]<=a[N]视情况而定。
    a[++N] = a[i];
}



代码如下:

#include<bits/stdc++.h>
using namespace std;
typedef long long LL;
const double eps = 1e-6;
const int INF = 2e9;
const LL LNF = 9e18;
const int mod = 1e9+7;
const int maxn = 2e5+10;

LL n,k,s,t, g[maxn];
LL N;

struct node
{
    LL c,v;
    bool operator<(const node&x)const{
        return v<x.v;
    }
}a[maxn];

void init()
{
    cin>>n>>k>>s>>t;
    for(int i = 1; i<=n; i++)
        scanf("%lld%lld",&a[i].c, &a[i].v);
    for(int i = 1; i<=k; i++)
        scanf("%lld",&g[i]);

    g[++k] = s; //将终点也放进去
    sort(g+1,g+1+k);

    N = 0;
    sort(a+1,a+1+n);    //按车的汽油容量升序排序
    for(int j = 1; j<=n; j++)   //单调队列
    {
        while(N>=1 && a[j].c<=a[N].c) N--;
        a[++N] = a[j];
    }
}

int test(int pos)
{
    LL x, y, T = 0; //x为以快速行驶的路程, y为以常速行驶的路程
    LL capa = a[pos].v;
    for(int i = 1; i<=k; i++)
    {
        LL dis = g[i]-g[i-1];

        if(capa<dis)   //以常速都跑不完
            return 0;
        else if(capa<2*dis )   //快速+常速 or 常速
            x = capa - dis, y = capa - 2*x;
        else                //可以全程以快速行驶
            x = dis, y = 0;

        T += x + 2*y;   //更新时间
        if(T>t) return 0;   //超时
    }
    return 1;
}

void solve()
{
    LL l = 1, r = N;
    while(l<=r)
    {
        LL mid = (l+r)>>1;
        if(test(mid))
            r = mid - 1;
        else
            l = mid + 1;
    }
    printf("%lld\n",l==N+1? -1 : a[l].c);
}

int main()
{
   init();
   solve();
}


三电平Buck直流变换器双闭环、单闭环、开环仿真【附参考文献】(Simulink仿真实现) 内容概要:本文档系统介绍了三电平Buck直流变换器在Simulink环境下的开环、单闭环与双闭环控制系统的建模仿真方法,涵盖系统建模、控制策略设计及动态性能对比分析。通过构建不同控制结构,深入探讨电压调节精度、系统响应速度、抗干扰能力及稳定性差异,重点剖析双闭环控制中电压外环与电流内环的协同作用机制,揭示其在抑制输出纹波、提升负载适应性方面的优势,帮助读者掌握多电平DC-DC变换器的核心控制原理与工程实现技术;文档还提供了详实的仿真参数设置与参考文献,便于理论验证与实践复现。; 适合人群:电力电子、自动化、电气工程及其相关专业的高校本科生、研究生、科研人员,以及从事电源系统、新能源变换器开发的工程技术人员。; 使用场景及目标:①作为高校电力电子课程的教学案例,辅助学生理解Buck变换器多层次控制策略的设计思路与性能差异;②为科研项目中高效直流变换系统的仿真研究提供可复用的技术模板;③指导工业界在高精度电源设计中优化控制器参数,提升系统动态响应与稳定性。; 阅读建议:建议结合Simulink仿真模型同步操作,对照文档逐步搭建各控制结构,重点观察不同闭环配置下输出电压的瞬态与稳态表现,深入理解PI参数整定、环路耦合影响及系统频域特性的关联性,从而全面提升对电力电子系统控制设计的综合把握能力。 立即下载

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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 5178 pairs —— 思维 + 二分

题目链接:http://acm.hdu.edu.cn/showproblem.php?pid=5178 pairs Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others) Total Submission(s): 3090    Accepted Submissio

A man can be destroyed, but not defeated. 517

Codeforces Round #398 (Div. 2) D. Cartons of milk —— 二分

题目链接:http://codeforces.com/contest/767/problem/D 代码如下: #include #include #include #include #include #include #include #include #include #include #include #define LL long long #define MAX(a,b) (a

A man can be destroyed, but not defeated. 510

HihoCoder - 1514 偶像的条件 —— 二分

题目链接:https://vjudge.net/problem/HihoCoder-1514 小Hi的学校正面临着废校的大危机。面对学校的危机,小Hi同学们决定从ABC三个班中各挑出一名同学成为偶像。   成为偶像团体的条件之一,就是3名团员之间的身高差越小越好。   已知ABC三个班同学的身高分别是A1..AN, B1..BM 和 C1..CL。请你从中选出3名同学Ai,

A man can be destroyed, but not defeated. 483

HDU5968 异或密码 —— 二分 + 边界的细节处理

题目链接:http://acm.hdu.edu.cn/showproblem.php?pid=5968 异或密码 Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 65536/65536 K (Java/Others) Total Submission(s): 1214    Accepted Submission

A man can be destroyed, but not defeated. 464

Codeforces Round #402 (Div. 2) D String Game —— 二分法

D. String Game time limit per test 2 seconds memory limit per test 512 megabytes input standard input output standard output Little Nastya has a hobby, she likes to remo

A man can be destroyed, but not defeated. 401

CodeChef Forest Gathering —— 二分

题目链接:https://vjudge.net/problem/CodeChef-FORESTGA 题解: 现场赛。拿到这题很快就知道是二分,但是一直wa,怎么修改也wa,后来又换了种错误的思路,最后还是觉得二分是正确的,但还是wa。人生,就是在这些瞬间被怀疑的…… 结束后,才发现,test()的时候溢出了。应该把判断放在循环里面,如果达到要求的,就立刻返回。因为达到要求后,

A man can be destroyed, but not defeated. 376

Codeforces Round #379 (Div. 2) C. Anton and Making Potions —— 二分

题目链接:http://codeforces.com/contest/734/problem/C C. Anton and Making Potions time limit per test 4 seconds memory limit per test 256 megabytes input standard input outp

A man can be destroyed, but not defeated. 351

Codeforces Round #143 (Div. 2) C. To Add or Not to Add —— 二分

题目链接:http://codeforces.com/problemset/problem/231/C 题解: n:1~100000,由于数据量大 ,用O(n^2)的方法肯定超时,那么就考虑用O(nlogn),看到logn,自然想到二分。于是就用二分法。logn为二分个数,n为枚举答案值。所以就是nlogn了。(如果二分 答案值,则不能判断当前答案值是否合法来缩小范围) 注意用前缀和来

A man can be destroyed, but not defeated. 345
上一篇: Codeforces Round #417 (Div. 2) B. Sagheer, the Hausmeister —— DP
下一篇: Educational Codeforces Round 9 C. The Smallest String Concatenation —— 贪心 + 字符串
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