poj 3368 离散化+线段树+二分

C#安装OpenCv及打开笔记本默认摄像头方法 阅读详情

Frequent values
Time Limit: 2000MS Memory Limit: 65536K
Total Submissions: 13911 Accepted: 5098

Description

You are given a sequence of n integers a1 , a2 , ... , an in non-decreasing order. In addition to that, you are given several queries consisting of indices i and j (1 ≤ i ≤ j ≤ n). For each query, determine the most frequent value among the integers ai , ... , aj.

Input

The input consists of several test cases. Each test case starts with a line containing two integers n and q (1 ≤ n, q ≤ 100000). The next line contains n integers a1 , ... , an (-100000 ≤ ai ≤ 100000, for each i ∈ {1, ..., n}) separated by spaces. You can assume that for each i ∈ {1, ..., n-1}: ai ≤ ai+1. The following q lines contain one query each, consisting of two integers i and j (1 ≤ i ≤ j ≤ n), which indicate the boundary indices for the 
query.

The last test case is followed by a line containing a single 0.

Output

For each query, print one line with one integer: The number of occurrences of the most frequent value within the given range.

Sample Input

10 3
-1 -1 1 1 1 1 3 10 10 10
2 3
1 10
5 10
0

Sample Output

1
4
3

Source

给出一个单调非减序列,询问指定 区间出现最频繁的值的出现次数。

利用线段树做,每个节点保存出现的最大频数cnt,假如查询区间[a,b],对于区间为[l,r]的节点,如果[a,b]在其[l,r]左半部分(b<=m),则递归到左子树求解,若在又右半部分(a>m)则递归到右子树。

假如a<=m<b,则查询左子树[a,m]的最大值和右子树[m+1,b]的最大值 cnt = max(lchild.cnt, rchild.cnt)。由于是单调非减的,所以出现次数最多的数可能一部分在左半区间的末端,另一部分在右半区间的起始端,所以要判断if(data[m]==data[m+1])  /*数据都存在data数组中*/,不等于(也就是小于)直接返回cnt。如果等于二分查找data[m]在左半区间出现的次数,和右半区间的次数。更新cnt最大值

这个方法交c++ 1079ms。。。

#include <iostream>
#include <cstring>
#include <cstdio>
#include <algorithm>

using namespace std;

#define maxn 100005

struct node
{
    int l, r;
    int cnt;

    node() {}
    node(int ll, int rr){ l = ll; r = rr;}
};

int data[maxn];
node tr[4*maxn];

void build(int rt, int l, int r)
{
    tr[rt] = node(l, r);
    if(l == r){
        tr[rt].cnt = 1;
        return;
    }

    int m = l+(r-l)/2;
    int lc = 2*rt, rc = 2*rt+1;
    build(lc, l, m);
    build(rc, m+1, r);

    tr[rt].cnt = max(tr[lc].cnt, tr[rc].cnt);

    if(data[m] == data[m+1]){
        int v = data[m];
        int cnt = m -(lower_bound(data+l, data+m+1, v)-data)+1;
        cnt += (upper_bound(data+m+1, data+r+1, v)-data)-(m+1);
        tr[rt].cnt = max(tr[rt].cnt, cnt);
    }
}


int query(int rt, int ll, int rr)
{
    int l = tr[rt].l, r = tr[rt].r;
    if(l == ll && r == rr)
        return tr[rt].cnt;

    int m = l+(r-l)/2;
    int lc = 2*rt, rc = 2*rt+1;
    if(rr <= m)
        return query(lc, ll, rr);
    if(ll > m)
        return query(rc, ll, rr);

    int ret = max(query(lc,ll, m), query(rc, m+1, rr));
    if(data[m] == data[m+1]){
        int v = data[m];
        int cnt = m -(lower_bound(data+ll, data+m+1, v)-data)+1;
        cnt += (upper_bound(data+m+1, data+rr+1, v)-data)-(m+1);
        ret = max(cnt, ret);
    }

    return ret;
}

int main()
{
    int n, q;
    while(~scanf("%d", &n) && n){
        scanf("%d", &q);
        for(int i = 1; i <= n; i++)
            scanf("%d", data+i);

        build(1, 1, n);
        int a,b;
        for(int i = 0; i < q; i++){
            scanf("%d%d", &a, &b);
            printf("%d\n", query(1, a, b));
        }
    }
    return 0;
}

还有一种做法是将数据进行离散化处理,将相同的数合并,并记录每个数出现的次数,再建立线段树记录出现的最大次数。每次询问[a,b],利用前缀和cnt[n]预处理计算前n个数总共出现多次,每次先二分查找a和b对应的下标位置l和r,对于完全包含的a,b里面的数[l+1,r-1]用线段树查询最大值,再跟cnt[l]-a+1, b-cnt[r-1]进行比较得到最大值

用c++跑625ms....

#include <iostream>
#include <cstring>
#include <cstdio>
#include <algorithm>

using namespace std;

#define maxn 100005

struct node
{
    int l, r;
    node(){}
    node(int ll , int rr) {l = ll; r = rr;}
};

node tree[4*maxn];
int maxi[4*maxn];
int data[maxn];
int cnt[maxn];

void build(int rt, int l, int r)
{
    tree[rt] = node(l, r);

    if(l == r){
       maxi[rt] = cnt[l]-cnt[l-1];
       return;
    }

    int m = l+(r-l)/2;
    int lc = 2*rt, rc = 2*rt+1;
    build(lc, l, m);
    build(rc, m+1, r);
    maxi[rt] = max(maxi[lc], maxi[rc]);
}

int query(int rt, int ll, int rr)
{
    if(ll > rr) return 0;
    int l = tree[rt].l, r = tree[rt].r;

    if(l == ll && r == rr)
        return maxi[rt];

    int m = l+(r-l)/2;
    if(rr <= m)
        return query(2*rt, ll, rr);
    else if(ll > m)
        return query(2*rt+1, ll, rr);
    else
        return max(query(2*rt, ll, m), query(2*rt+1, m+1, rr));
}

int main()
{
    int n, q;
    while(~scanf("%d",&n) && n){
        scanf("%d", &q);
        for(int i = 1; i <= n; i++)
            scanf("%d", data+i);
        data[0] = data[1];

        int cur = 1;
        int tot = 0;
        cnt[0] = 0;
        for(int i = 1; i <= n; i++){
            if(data[i] == data[i-1])
                tot++;
            else{
                data[cur] = data[i-1];
                cnt[cur] = cnt[cur-1]+tot;
                tot = 1;
                cur++;
            }
        }
        cnt[cur] = cnt[cur-1]+tot;
        build(1, 1, cur);

        int a, b;
        for(int i = 0; i < q; i++){
            scanf("%d%d", &a, &b);
            int l = lower_bound(cnt+1, cnt+cur+1, a)-cnt;
            int r = lower_bound(cnt+1, cnt+cur+1, b)-cnt;

            if(l == r)
                printf("%d\n",b-a+1);
            else{
                int ans;
                ans = query(1, l+1, r-1);
                ans = max(cnt[l]-a+1, ans);
                ans = max(ans, b-cnt[r-1]);
                printf("%d\n", ans);
            }
        }
    }
    return 0;
}


计算机组成原理(第三版)唐朔飞-第七章指令系统-课后习题 机器字长是指CPU一次能处理数据的位数通常与CPU的寄存器位数有关。指令字长是指机器指令中二进制代码的总位数。存储字长是指存储单元中存放二进制代码的总位数。三者可以相等也可以不等视不同机器而定。答:OP(4位)A1(6位)A_1(6位)A1​(6位)A2(6位)A_2(6位)A2​(6位)设二地址指令格式为该指令系统的基本格式,4位操作码共有16种编码,其中13种用来定义二地址指令,还剩3种可用作扩展标志。一地址指令条数=3∗26=192。 阅读详情

相关推荐

sap 新增科目表_SAP系统中四大科目表的总结

操作科目表定义-OB13 2国家科目表定义-OB13 3集团科目表定义-OB13 4合并科目表(ECCS)定义-CX10 4分配操作科目表到公司代码-OB62 5分配国家科目表到公司代码-OB62 6分配集团科目表到操作科目表-OB13 6分配集团科目表到合并科目表-CXNF 7Operation 8定义集团科目表的科目-FSP0 8定义国家科目表的科目-FSP0 8定义操作科目表的科目-FSP0...

weixin_39585463的博客 1721

【POJ1177】Picture-线段树+离散化+扫描线

【POJ1177】Picture-线段树+离散化+扫描线

Maxwei_wzj的OI世界 459

银河麒麟V10系统下,手把手解决Ollama安装报错‘GLIBCXX_3.4.25 not found’

本文详细介绍了在银河麒麟V10系统下解决Ollama安装报错‘GLIBCXX_3.4.25 not found’的实战指南。通过隔离编译新版libstdc++、环境变量引导和服务化封装等安全方案,确保在不破坏系统稳定性的前提下顺利运行Ollama,适用于国产操作系统与AI工具的兼容性需求。

weixin_42556197的博客 484

HDU 4006 离散化+权值线段树+线段树二分

The kth great number   题目大意: I操作表示在数列中添加一个数(序列最初为空) Q操作表示求数列中第k大的数 解题思路: 由于要求数列中第k大的数,因此考虑使用权值线段树。(详见  UESTC 841 权值线段树 ) 但是数据范围是10^6,考虑离散化。 枚举每一次操作,将选出的数压入线段树。求后缀和为k的数的编号: 线段树二分

qq_38136552的博客 675

无网络环境部署:手动获取并安装OneNote for Windows 10完整指南

本文提供了一份详尽的OneNote for Windows 10离线安装指南。针对无网络或内网隔离环境,详细介绍了如何从官方渠道获取正确的产品ID,利用第三方工具下载纯净的.appxbundle安装包,并解决系统兼容性与依赖框架问题。最后,通过PowerShell管理员命令完成安全部署,是IT管理员和高级用户在离线环境下部署UWP应用的实用参考。

loss4bartender的博客 496

STM32L051低功耗模式下LPUART的使用

STM32L0系列低功耗UART配置代码清单及注释,大家可参考学习使用。

嵌入式工程师 2104

POJ 1177-Picture(线段树+离散化+扫描线)

Picture Time Limit: 2000MS   Memory Limit: 10000K Total Submissions: 12672   Accepted: 6699 Description A number of rectangular posters, photographs and other pictures of

信仰.的博客 693

这个AI工具可以把产品说明书转成演示视频,老板狂喜

回顾本文,我们从“传统视频制作的痛点”出发,拆解了AI将产品说明书转为视频的核心技术(NLP文本理解→CV画面生成→视频合成),并提供了3类工具实操方案(SaaS/开源/API)。推广成本降80%,制作效率提90%,产品迭代与推广终于能“同频”;摆脱对专业团队的依赖,自己就能快速产出高质量视频;对技术爱好者来说,这是一个“AI多模态融合”的绝佳实践案例(NLP+CV+语音合成的综合应用)。今天就找一份产品说明书,用HeyGen或Pictory试做一个视频。

AIGC应用创新大全的博客 322

【HDOJ】 Picture (离散化+线段树)

【HDOJ】 Picture (离散化+线段树) Picture Time Limit: 6000/2000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others) Total Submission(s): 3898    Accepted Submission(s): 1979 Problem Descri

ACMer' 1405

YOLOv11改进策略【Conv和Transformer】| IJCAI 2024 利用FreqFormer中的SFA 空间 - 频率注意力和CTA 通道转置注意力 二次改进C3k2

在FreqFormer模型中,SFA(Spatial-Frequency Attention)和CTA(Channel Transposed Attention)模块是关键组成部分,它们针对传统Transformer在图像超分辨率任务中的不足而设计,旨在更好地恢复高频细节,提升模型性能。

Limiiiing的博客 758

Power EM与Signal EM解决办法与原理分析

1.面对power EM,首先检查missing via,补全via,还可以加宽,加密局部power mesh,一些power IO出pin有power EM问题,可以叠加多层线和垂直方向补power形成网络降低并联电阻和提供更多电流通路解决,这种方法还可以降低ir drop。3.先进工艺有一个via ladder的方法可以解决power/signal em问题,clock cell出pin绕线或者power rail连接power stripe通过搭梯子的方式连接,原理还是分流。

m0_61544122的博客 4745

poj1177 Picture 线段树+扫描线+离散化

题目链接:http://poj.org/problem?id=1177题意: 题目就是说n个矩形组成的新图形的周长,每个矩形会给出左下角和右上角的坐标。分析: 线段树+扫描线+离散化,我是从左到右扫描的,需要将纵坐标离散化,然后每扫描到一根线,求出其覆盖y方向的总长度,减去上次求得的长度,即为本条线增加或减少的长度,同时可以求出每两条线之间的距离,即为横坐标方向的

lqdandelion的博客 1994

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")

poj 3368

最近写的程序不是超时就是WA,而且都找不到原因,还好这道题是自己从超时里面优化出来的,尽管还是很慢啊! 题是利用离散化和线段树,别的不说了,看代码吧 #include #include #include #include #include #include #include #include #include using namespace std; #define ll __int64 #d

生活总是要微笑着面对~ 466

STM32F407中文手册

STM32f407中文手册 本参考手册面向应用开发人员,提供有关使用 STM32F405xx/07xx、STM32F415xx/17xx、 STM32F42xxx 和 STM32F43xxx 微控制器存储器与外设的完整信息。STM32F405xx/07xx、STM32F415xx/17xx、STM32F42xxx 和 STM32F43xxx 构成一个微 控制器系列,各产品具有不同的存储器大小、封装和外设。有关订购信息以及器件的机械与电气特性,请参见数据手册。有关 ARM Cortex™-M4F 内核的信息,请参见《Cortex™-M4F 技术参考手册》。

visio安装包

Microsoft Office Visio 是一款专业的制图软件。利用经过更新的形状、协作工具和数据链接图表,创建专业级图表以简化复杂的信息。

poj3368 frequent values (快状数组)

poj3368 frequent values 二分+线段树可过 求大神传授人生经验

Gach_acmer的博客 414

VB摄像头监控

该VB模块可对摄像头捕获的画面进行录像等,可在创建的窗口进行控制

POJ3368——Frequent values(RMQ)

Frequent values Time Limit: 2000MS   Memory Limit: 65536K Total Submissions: 13535   Accepted: 4981 Description You are given a sequence of n integers a1 , a2 , ... , a

Reverie 528

线段树

线段树 博客推荐: 夜深人静写算法-线段树 线段树详解 线段树一个很大的用途就是用于求解RMQ问题,就是在一个区间中查询最值 线段树分为很多类型有区间最值区间求和,还有单点更新,区间更新。。。 模板: 初始化树: void init(int id, int start, int end){ if(start == end){ node[id].value = input[start]; }...

Two_Punch的博客 329

POJ上题目分类

看了许多大牛的博客,学到了经典常用算法要尽量多敲和记住。唉,感觉挺难的~不过,我会尽量照做的。 I am a slow walker, but I never come back. 中级:  一.基本算法:  (1)C++的标准模版库的应用. (poj3096,poj3007)  (2)较为复杂的模拟题的训练(poj3393,poj1472,poj3371,poj

coco_astrids的博客 382

POJ专项——解题报告

这个是参考业界广为流传的

zhengzaishen的博客 441

POJ题目分类

中级: 一.基本算法:  (1)C++的标准模版库的应用. (poj3096,poj3007) (2)较为复杂的模拟题的训练(poj3393,poj1472,poj3371,poj1027,poj2706,poj1009) 二.图算法:  (1)差分约束系统的建立和求解. (poj1716,poj1201,poj2983) (2)最小费用最大流(poj2516,poj2195) (3

天亮说晚安 1906

poj1753 php,POJ训练计划

第一阶段 初级:第1周-第4周 92道题项目时间必做题目基本算法枚举第1周poj1753,poj2965贪心poj1328,poj2109,poj2586分治法poj2524递推poj2506构造法poj3295模拟法poj1068,poj2632,poj1573,poj2993,poj2996图算法图的深度优先遍历和广度优先遍历第1周poj3278, poj2049, poj3083最短路径算法...

weixin_39936310的博客 144

题目合集(仅供参考)

算法题目推荐

2302_79599802的博客 1204

poj题目分类

// poj题目分类 http//poj.org/ 初期 基本算法 枚举 1753 2965 贪心1328 2109 2586 递归和分治法 递推 构造法 3295 模拟法 1068 2632 1573 2993 2996 图算法 图的深度优先遍历和广度优先遍历 最短路径算法dijkstra bellman-ford floyd heap...

weixin_30815469的博客 99

还是POJ 题目分类,我在想做这个分类的是不是把题目都看完了。。。。英语啊!!

初期: 一.基本算法: (1)枚举. (poj1753,poj2965) (2)贪心(poj1328,poj2109,poj2586) (3)递归和分治法. (4)递推. (5)构造法.(poj3295) (6)模拟法.(poj1068,poj2632,poj1573,poj2993,poj2996) 二.图算法: (1)图的深度优先遍历和广度优先遍历. (2)最短路

ACM菜鸟的点滴_CXB 604

考试题目

参考题目: Sotong的practice题目:64个,这些题目做熟练了,很多算法就差不多了。   另外补充一些:POJ的题目:   第二阶段中级(以图、树为重点): 项目 时间 必做题目 基本算法 C++的标准模版库的应用   poj3096,poj3007  

leocat2011的专栏 422

POJ题目分类【实在是不知道哪个是原创了】

初期: 一.基本算法: (1)枚举. (poj1753,poj2965) (2)贪心(poj1328,poj2109,poj2586) (3)递归和分治法. (4)递推. (5)构造法.(poj3295) (6)模拟法.(poj1068,poj2632,poj1573,poj2993,poj2996) 二.图算法: (1)图的深度优先遍历和广度优先遍历. (2)最短路径算法(d

有一种力量叫乐观 589

训练计划

第一阶段 初级:第1周-第2周(共80题)项目时间必做题目基本算法枚举第1周poj1753,poj2965贪心poj1328,poj2109,poj2586分治法 递推 构造法poj3295模拟法poj1068,poj2632,poj1573,poj2993,poj2996图算法图的深度优先遍历和广度优先遍历第1周 最短路径算法poj1860,poj3259,poj1062,poj2253,poj...

419
上一篇: poj 3264 st算法
下一篇: poj 3111 二分求最大化平均值
HuanTongH
博客等级 码龄12年 7粉丝 110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