LeetCode Best Time to Buy and Sell Stock IV

Immich vs Nextcloud照片管理实测:自建私有云相册到底选哪个? 本文深度评测了Immich与Nextcloud在私有云相册管理中的表现,涵盖核心功能、性能基准、本土化实践及进阶功能。Immich凭借微服务架构和AI优化在照片管理、移动端体验上表现优异,而Nextcloud则强在生态整合。文章还提供了Docker部署优化和国内网络适配方案,帮助用户根据需求选择最佳方案。 阅读详情

原题链接在这里:https://leetcode.com/problems/best-time-to-buy-and-sell-stock-iv/

本题是Best Time to Buy and Sell Stock III的general情况。

但这里要注意若是k很大已经超过了prices.length的时候,若是按照DP的方法做会浪费时间以及空间。

此时可以直接参照Best Time to Buy and Sell Stock II的做法。

AC Java:

public class Solution {
    public int maxProfit(int k, int[] prices) {
        if(prices == null || prices.length == 0){
            return 0;
        }
        if(k>=prices.length){
            int res = 0;
            for(int i = 1; i < prices.length; i++){
                res += Math.max(0, prices[i]-prices[i-1]);
            }
            return res;
        }
        
        int[] local = new int[k+1];
        int[] global = new int[k+1];
        for(int i = 1; i<prices.length; i++){
            int diff = prices[i] - prices[i-1];
            for(int j = k; j>=1; j--){
                local[j] = Math.max(global[j-1] + Math.max(diff,0), local[j] + diff);
                global[j] = Math.max(global[j], local[j]);
            }
        }
        return global[k];
    }
}


海康Visionmaster-全局脚本:VM 加载方案后自动执行 的方法,如何让VM在方案加载后自动执行流程? 阅读详情

相关推荐

【阿里云大模型高级工程师ACP习题集】2.4 自动化评测答疑机器人的表现(⭐️⭐️⭐️ 重点章节!!!)

【摘要】这是我精心整理出对应的习题集,里面足足有 30 道题目,从基础概念到深度拓展,全方位覆盖章节重点、难点。不管你是想检验自己的学习成果,还是想针对薄弱环节进行强化训练,它都能完美适配!

davidkorhenn的博客 2477

LeetCode:188. Best Time to Buy and Sell Stock IV - Python

LeetCode:188. Best Time to Buy and Sell Stock IV(买卖股票的最佳时机 )问题描述:给定一个数组,它的第i个元素是一支给定的股票在第i天的价格。设计一个算法来计算你所能获取的最大利润。你最多可以完成k笔交易。 注意:你不能同时参与多笔交易(你必须在再次购买前出售掉之前的股票)。示例1:输入: [2,4,1], k = 2 输出: 2...

Growth Diary 2179

YOLOv8改进策略【损失函数篇】| 2024 引进Focaler-IoU损失函数 加强边界框回归 (Focaler-DIoU、Focaler-GIoU、Focaler-CIoU)

本文记录的是改进YOLOv8的损失函数,将其替换成。现有研究通过利用边界框之间的几何关系来提高回归性能,但忽略了困难样本和简单样本分布对边界框回归的影响。不同检测任务中困难样本和简单样本的分布不同,对于简单样本占主导的检测任务,关注简单样本的边界框回归有助于提高检测性能;对于困难样本比例较高的检测任务,需要关注困难样本的边界框回归。,提高检测器在不同检测任务中的性能。实现的。

Limiiiing的博客 1221

leetcode 188. Best Time to Buy and Sell Stock IV

/二是第i天买入了第j只股票(需支付prices[i]),第i天能买入第j只股票的前提是前一天(第i-1天)结束时处于已经卖出第j-1只股票但是还没有买入第j只股票的状态(对应dp[i-1][j-1].sold)。//二是第i天卖出了第j只股票(收入prices[i]),第i天能卖出第j只股票的前提是前一天(第i-1天)结束时处于持有第j只股票的状态(对应状态dp[i-1][j].holding)//到第0天结束时处于持有第j只股票的状态,可以理解为对第0天的股票买了又卖重复j-1次之后再买入。

weixin_41554427的博客 1349

机器人控制仿真软件:MATLAB Simulink Control System Toolbox_(1).MATLAB基础知识

MATLAB(Matrix Laboratory)是由MathWorks公司开发的一种高性能数值计算和可视化软件。它广泛应用于科学计算、工程设计、信号处理、控制系统设计、图像处理等领域。强大的矩阵运算能力:MATLAB的核心是矩阵运算,能够高效处理大规模的数值数据。丰富的工具箱:MATLAB提供了大量的工具箱,如Control System Toolbox、Simulink、Signal Processing Toolbox等,这些工具箱覆盖了多个应用领域。易于使用的图形界面。

kkchenjj的博客 904

​​手把手教你学PCIE--ATU 的 Bar 匹配模式与地址匹配模式详解​

摘要: ATU(地址转换单元)提供两种地址匹配模式:Bar匹配模式基于PCI设备的BAR寄存器自动映射虚拟地址(只需配置物理基址),适用于标准PCI设备DMA;地址匹配模式需显式配置虚拟与物理地址范围,适合自定义映射场景。核心区别在于匹配依据(BAR自动推导vs人工定义),Bar模式简化配置,地址模式更灵活。

MHD0815的博客 379

Best Time to Buy and Sell Stock IV - LeetCode

Best Time to Buy and Sell Stock IV - LeetCode 题目: Say you have an array for which the ith element is the price of a given stock on day i. Design an algorithm to find the maximum profit. You may

SGAFPZ的博客 412

YOLOv11改进策略【模型轻量化】| 替换骨干网络为 GhostNet V3 2024华为的重参数轻量化模型

一、本文介绍 本文记录的是基于 GhostNetV3 的 YOLOv11 轻量化改进方法研究。GhostNetV3的轻量模块采用重参数化方法,训练时为深度可分离卷积和1×1卷积添加线性并行分支,推理时通过逆重参数化移除分支、折叠操作,能够在不增加推理成本的同时提高性能,从而实现YOLOv11的轻量化改进。 专栏目录:YOLOv11改进目录一览 | 涉及卷积层、轻量化、注意力、损失函数、Backbone、SPPF、Neck、检测头等全方位改进 专栏地址:YOLOv11改进专栏——以发表论文的角度,快速

Limiiiing的博客 1844

[LeetCode] Best Time to Buy and Sell Stock IV

Say you have an array for which the ithi^{th} element is the price of a given stock on day ii.Design an algorithm to find the maximum profit. You may complete at most k transactions.Note: You may not

楚兴 1万+

51-14 Retentive Network,RetNet 多尺度保留机制序列建模论文精读

RetNet 提出了多尺度保留机制序列建模,该机制支持三种计算范式,即并行、循环和分块循环。RetNet有更好的全局注意力建模、高效的计算效率、对遮挡的鲁棒性、支持基于注意力的图像生成、与置信度相关的门控机制和多任务学习等特点,对不同视觉任务可以共享历史注意力记忆,可以同时进行分类、分割、检测等任务。RetNet在视觉领域应该有广阔的应用前景。

AIgraphX 1613

从 VL02N 拣配到 ABAP 自动化,读懂 SD_DELIVERY_UPDATE_PICKING 的位置和风险

文章摘要:SAP SD模块中,SD_DELIVERY_UPDATE_PICKING函数模块用于处理出库交货单的拣配确认,更新交货项目的拣配数量。该函数适用于未启用WM/EWM的传统SD流程,能确保拣配状态与标准交货处理逻辑同步,比直接修改数据库更安全。但在S/4HANA Cloud等新环境中,应优先使用API_OUTBOUND_DELIVERY_SRV等公开发布接口。使用该函数时需注意参数完整性和按交货单整体处理的原则,避免逐项提交导致的数据不一致问题。(149字)

2007 年 ~ 2025 年,深耕 SAP 技术 18 年 243

LeetCode: 188. Best Time to Buy and Sell Stock IV

LeetCode: 188. Best Time to Buy and Sell Stock IV 题目描述 Say you have an array for which the ith element is the price of a given stock on day i. Design an algorithm to find the maximum profit. You ma...

杨领well的专栏 507

别再让 AI 瞎写 SAP 代码了!这套 Claude Code Skill 的验证机制才是关键

本文深入解析了专为SAP CAP开发设计的AI辅助工具sap-cap-capire的技术架构。该工具采用四层设计:协议层集成MCP和LSP实现实时模型访问与语法检查;智能层通过4个专业化Agent(数据建模、服务开发等)分工协作;验证层确保代码质量;知识层存储文档模板。这种工程化设计解决了通用AI在CAP开发中面临的上下文不足、专业性缺失和代码质量保证等核心问题,通过本地化服务和实时反馈机制显著提升了开发效率。

2007 年 ~ 2025 年,深耕 SAP 技术 18 年 880

【LeetCode】188. Best Time to Buy and Sell Stock IV 解题报告(Python)

作者: 负雪明烛 id: fuxuemingzhu 个人博客: http://fuxuemingzhu.cn/ 目录题目描述题目大意解题方法日期 题目地址:https://leetcode.com/problems/best-time-to-buy-and-sell-stock-iv/description/ 题目描述 Say you have an array for which the it...

负雪明烛 3949

ET2012新版免狗免安装绿色软件

ET2012新版免狗免安祖昂绿色软件。,新界面,新视觉,

lwip-2.0.3

lwip是瑞典计算机科学院(SICS)的Adam Dunkels 开发的一个小型开源的TCP/IP协议栈。实现的重点是在保持TCP协议主要功能的基础上减少对RAM 的占用。

leetcode算法-Best Time to Buy and Sell Stock IV

leetcode算法-Best Time to Buy and Sell Stock IV题目:给定一个数组prices[n],其中prices[i]代表第i天股票的价格,限定最多进行k次交易(买进和卖出股票算一次交易),在卖出之前必须进行买入操作,求股票交易的最大收益。涉及算法:动态规划主要思路:(主要是看了大佬的加上自己的理解)我们需要一个数组dp[k+1][prices.length]来记录...

xiaodidadada的博客 181

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")

leetcode 第188题,我的解法,Best Time to Buy and Sell Stock IV

leetcode第188题,Best Time to Buy and Sell Stock IV题目如下: https://leetcode.com/problems/best-time-to-buy-and-sell-stock-iv/ 使用了最大堆

郭腾虎的专栏 628

ssm框架搭建

这个是一个在idea上成功跑起来的ssm框架项目,保存在这里方便以后使用直接下载

【LeetCode】解题188:Best Time to Buy and Sell Stock IV(动态规划)

LeetCode解题 188:Best Time to Buy and Sell Stock IV (动态规划)Problem 188: Best Time to Buy and Sell Stock IV [Hard]解题思路Solution (Java)修改过程 Problem 188: Best Time to Buy and Sell Stock IV [Hard] Say you hav...

Fayedily的博客 596

【LeetCode】 Best Time to Buy and Sell Stock I II III IV 解题报告

Best Time to Buy and Sell Stock I题意:用一个数组表示股票每天的价格,数组的第i个数表示股票在第i天的价格。 如果只允许进行一次交易,也就是说只允许买一支股票并卖掉,求最大的收益。分析:动态规划法。从前向后遍历数组,记录当前出现过的最低价格,作为买入价格,并计算以当天价格出售的收益,作为可能的最大收益,整个遍历过程中,出现过的最大收益就是所求。**代码:**O(n)时

跳出温水的青蛙 2万+

LeetCode(188) Best Time to Buy and Sell Stock IV (Java)

题目如下: Best Time to Buy and Sell Stock Total Accepted: 43912 Total Submissions: 135635 My Submissions Question Solution  Say you have an array for which the ith element is the price of a given stock

BUILD 7494

LeetCode //C - 188. Best Time to Buy and Sell Stock IV

【代码】LeetCode //C - 188. Best Time to Buy and Sell Stock IV。

Made in Code 1152

LeetCode188 Best Time to Buy and Sell Stock IV

Say you have an array for which theithelement is the price of a given stock on dayi. Design an algorithm to find the maximum profit. You may complete at mostktransactions. Note: You may not enga...

李歇特冯-兹拜因巴哈的博客 216

[LeetCode]Best Time to Buy and Sell Stock IV

https://leetcode.com/problems/best-time-to-buy-and-sell-stock-iv/ 要求交易几次就设置几对hold&release状态。hold表示在该点买入,release表示在该点卖出。初始状态填充hold为极小值。 hold和release的值表示的是“我”当前所持有的钱数。因此如果hold就要以前一个release减去当前股票市值

gqk289的专栏 514

[leetcode] Best Time to Buy and Sell Stock IV

Problem Description: Say you have an array for which the ith element is the price of a given stock on day i. Design an algorithm to find the maximum profit. You may complete at most k transact

退而结网 583

Leetcode之Best Time to Buy and Sell Stock IV

题目: Say you have an array for which thei-thelement is the price of a given stock on dayi. Design an algorithm to find the maximum profit. You may complete at mostktransactions. Note: You may n...

qq_35455503的博客 133

[Leetcode][Python] Best Time to Buy and Sell Stock I,II,III,IV总结

[Leetcode][Python] Best Time to Buy and Sell Stock I,II,III,IV121. Best Time to Buy and Sell Stock I题目大意代码122. Best Time to Buy and Sell Stock II题目大意代码123. Best Time to Buy and Sell Stock III题目大意代码188. Best Time to Buy and Sell Stock IV题目大意代码 121. Best Tim

TwinkleZhao的博客 298

LeetCode-Best Time to Buy and Sell Stock IV(JAVA)

Say you have an array for which the i-th element is the price of a given stock on day i. Design an algorithm to find the maximum profit. You may complete at most k transactions. Note: You may not enga...

yajunandchunyun的专栏 288
上一篇: LeetCode Best Time to Buy and Sell Stock III
下一篇: LeetCode Largest Rectangle in Histogram
Dylan_Java_NYC
博客等级 码龄11年 1粉丝 134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