leetcode 410. Split Array Largest Sum(分割数组最大和)

LeetCode: 410. 分割数组的最大值 - Python 分割分数的最小值题目描述:给n个正整数a1, a2, a3, ... ... an,将n个数顺序排成一列后分割成m段,每一段的分数被记为这段内所有数的和,该次分割的分数则被记为m段分数的最大值。问所有的分割方案中分割分数的最小值是多少?其实本题,就是LeetCode的 410. Split Array Largest Sum。问题分析:二分查找(Binary Search)1、所给的数子都是正整数... 阅读详情

Given an array which consists of non-negative integers and an integer m, you can split the array into m non-empty continuous subarrays. Write an algorithm to minimize the largest sum among these m subarrays.

Note:
If n is the length of array, assume the following constraints are satisfied:

1 ≤ n ≤ 1000
1 ≤ m ≤ min(50, n)
Examples:

Input:
nums = [7,2,5,10,8]
m = 2
Output:
18

Explanation:
There are four ways to split nums into two subarrays.
The best way is to split it into [7,2,5] and [10,8],
where the largest sum among the two subarrays is only 18.

思路:
方法一:
二维DP

    public int splitArray(int[] nums, int m) {
        if(nums == null || nums.length == 0) {
            return Integer.MAX_VALUE;
        }
        int n = nums.length;
        int[][] dp = new int[m+1][n];
        dp[1][0] = nums[0];
        int[] sums = new int[n];
        sums[0] = nums[0];
        
        for(int i = 2; i <= m; i++) {
            Arrays.fill(dp[i], Integer.MAX_VALUE);
        }
        
        //只分成一组时为数组的和,dp[i][j]表示index=0~j的部分分成i组,最小的最大和
        for(int j = 1; j < n; j++) {
            sums[j] = sums[j-1] + nums[j];
            dp[1][j] = sums[j];         
        }
        
        for(int i = 2; i <= m; i++) {
            for(int j = i - 1; j < n; j ++) {
                for(int k = 0; k < j; k++) {
                //因为取min,所以dp初始要设为MAX_VALUE
                    dp[i][j] = Math.min(dp[i][j], Math.max(dp[i-1][k], sums[j] - sums[k]));
                }
                
            }
        }
        
        return dp[m][n-1];
        
    }

方法二:Binary search
左边界:数组中的最大元素,因为它本身不能再分割
右边界:数组元素的和,即分为一组的情况
找出分割数组的和mid,元素依次相加,大于mid时换一组,当组数<m时说明每个组的和较大,应分割成更小的组,这时更新right = mid-1
组数>m时说明应把每个组的和增大,减小组数,这时更新left = mid + 1
即使组数=m也应该持续更新,直到left > right, 因为可能会找到和更小的
当组数减小到不能再减小时,left就是最后最小的和

    public int splitArray(int[] nums, int m) {
        if(nums == null || nums.length == 0) {
            return Integer.MAX_VALUE;
        }
        
        int left = 0;
        int right = 0;
        //int sumV = 0;
        //int result = Integer.MAX_VALUE;
        int groups = 1;
        
        for(int i = 0; i < nums.length; i++) {
            left = Math.max(left, nums[i]);
            right += nums[i];
        }
        
        
        //binary search
        while(left <= right) {
            int mid = left + (right - left) / 2;
            int sum = 0;
            groups = 1;
            //sumV = 0;
            for(int i = 0; i < nums.length; i++) {
                sum += nums[i];
                if(sum > mid) {
                    groups ++;
                    //sumV = Math.max(sumV, sum - nums[i]);
                    //result = Math.min(result, sumV);
                    sum = nums[i];
                }
            }
            //sumV = Math.max(sumV, sum);
            
            if(groups > m) { //每组的和应该大一点,分组才能少一点
                left = mid + 1;
            } else { //每组和小一点,分组才能多一点,groups==m时还可以试试和能不能再小点
                right = mid - 1;
                
            }
        }
        
        return left;
    }
《Python星球日记》 第73天:情感分析与主题建模 《Python星球日记》 第73天:情感分析与主题建模,今天我们将探索两个自然语言处理领域的重要技术:情感分析和主题建模。这两项技术在社交媒体分析、用户反馈处理、市场研究等多个领域有着广泛应用。 阅读详情

相关推荐

波分复用-Zblock仿真

Z-BLOCK是一种用于光通信模块的光学元件,其核心结构是在玻璃块一侧贴有4个特定波长滤光片,另一侧镀有反射膜和增透膜。光信号在内部呈现Z字形反射路径,实现波长分离。文章以可见光LED为例,在ZEMAX中演示了非序列模式下的建模过程,重点介绍了滤光片和膜层的参数设置方法。需要注意的是,入射角度需根据玻璃折射率、通道高度和厚度精确计算,否则可能导致光泄漏。该建模方法同样适用于光通信中的光纤激光器系统。

Zhaxibamao的博客 1743

【Leetcode】410. Split Array Largest Sum(配数学证明)

段的分法,但由归纳假设,贪心法得到的段数已经是最小的了,矛盾)。个连续的子段,使得每个子段的和的最大值最小。的位置为止,这一段就分出来了,然后再开始下一段的累加,直到整个。接着反证法,如果存在,不妨设这种分法的第一段的长度。段的所有方案里,使得最大子段和最小那种方案的最大子段和。的和与前面分割的最大子段和取个最大,这些值再随着。是小于上面判定所使用的分法分出来的第一段长度。按照最后一段分的位置分类,可以将分为。的一段,直到恰好到最后一个小于等于。段的方案划分为,最后一段是。段,每段的和都小于等于。

数学、算法爱好者的博客 466

STM32 SYSTICK定时器及延时函数详解

STM32 SYSTICK及延时函数详解 SysTick定时器 SysTick定时器被捆绑在NVIC中,用于产生SYSTICK异常(异常号:15)。在以前,大多操作系统需要一个硬件定时器来产生操作系统需要的滴答中断,作为整个系统的时基。例如,为多个任务许以不同数目的时间片,确保没有一个任务能霸占系统;或者把每个定时器周期的某个时间范围赐予特定的任务等,还有操作系统提供的各种定时功能,都与这个滴答定时器有关。因此,需要一个定时器来产生周期性的中断,而且最好还让用户程序不能随意访问它的寄存器,以维持操作系统“心

w1050321758的博客 2365

LeetCode-410 Split Array Largest Sum|分割数组的最大值

LeetCode-410 Split Array Largest Sum|分割数组的最大值 题目描述 给定一个非负整数数组和一个整数 m,你需要将这个数组分成 m 个非空的连续子数组。设计一个算法使得这 m 个子数组各自和的最大值最小。 注意: 数组长度 n 满足以下条件: 1 ≤ n ≤ 1000 1 ≤ m ≤ min(50, n) 示例: 输入: nums = [7,2,5,10,8] m = 2 输出: 18 解释: 一共有四种方法将nums分割为2个子数组。 其中最好的方式是将其分为[7,2,5]

weixin_43265151的博客 412

进芯AVP28335烧录避坑全记录:CCS、Uniflash、C2Prog、SDFlash四大工具配置详解

本文详细解析了进芯AVP28335芯片在烧录过程中遇到的常见问题及解决方案,涵盖CCS、Uniflash、C2Prog和SDFlash四大工具的配置要点。针对国产器件AVP28335的特殊需求,提供了从开发环境准备到烧录验证的全流程指南,帮助开发者高效完成程序烧录,避免常见陷阱。

weixin_30836759的博客 586

vsomeip3 双机通信文件配置

验证环境 vsomeip地址Android运行命令,红色的地方需要和配置文件一样Linux运行命令,红色的地方需要和配置文件一样Linux service配置文件 ,红色的地方需要更改Android client配置文件,红色的地方需要更改,蓝色的地方可不要 ...

m0_37132481的博客 5222

410. Split Array Largest Sum 分割数组的最大值

给定一个非负整数数组和一个整数 m,你需要将这个数组分成 m 个非空的连续子数组。设计一个算法使得这 m 个子数组各自和的最大值最小。 注意: 数组长度 n 满足以下条件: 1 ≤ n ≤ 1000 1 ≤ m ≤ min(50, n) 示例: 输入: nums = [7,2,5,10,8] m = 2 输出: 18 解释: 一共有四种方法将nums分割为2个子数组。 其中最好的方式是将其分为[7,2,5] 和 [10,8], 因为此时这两个子数组各自的和的最大值为18,在所有情况中最小。 动态规划 看到「

Alex 676

图像做旧算法原理及代码解释

该图像做旧算法主要通过一系列的图像处理操作来模拟旧照片的效果。整体流程包括:首先对输入图像进行颜色调整,降低饱和度并增加黄色调以营造出旧照片的色彩风格;接着添加高斯噪声来模拟旧照片可能存在的颗粒感;最后通过在图像上随机绘制线条来模拟旧照片的划痕和瑕疵。综上所述,该图像做旧算法通过对颜色的调整(包括饱和度降低、色相偏移)、添加高斯噪声以及模拟划痕和瑕疵等一系列操作,从多个方面模拟了旧照片的特征。

m0_44975814的博客 901

leetcode410. Split Array Largest Sum

【代码】leetcode410. Split Array Largest Sum。

weixin_44245188的博客 462

Claude Code本地Windows安装教程

本文介绍了在Windows系统上安装和配置Claude Code本地调用的详细步骤。首先需要确保网络畅通和账号余额充足,然后安装Node.js v22版本作为基础环境。接着通过npm全局安装Claude Code包,并进行版本验证。安装完成后,使用命令行登录并可以指定模型启动。最后还提供了切换API秘钥的方法,通过setup-token命令重新授权账号。整个过程配有详细的命令行示例和操作截图,方便用户按步骤完成安装配置。

猿小白的博客 2万+

Unity 佳能SDK 及数据获取

3. 把CameraControl 下面只要是绿色的 .cs 文件都复制到Unity 中。1. 填写信息跟官方申请SDK,大概1-2个工作日会邮件回复你。2. 将SDK这两个文件放到 Unity Plugins文件夹。

我知道你很急,但是路要一步步走 1474

leetCode_Split Array Largest Sum

题意:把一个非整数数组分成若干个子数组,使得子数组和的最大值最小 题解:刚刚想起来,艾神曾经说过一看到最大值最小就得想到二分。好吧,以后区间求值都要往二分上去想,不过还是有动态规划的思路的。 方法一:动态规划(n^2超时) a[s][i](表示以nums[s]结尾并且前面分成i个数组)=min(a[s][i],max(a[k][i-1],sum[s]-sum[k])),其中(i-1 方法二

XiaYiZhanQiHang的博客 466

自注意力机制(SelfAttention Mechanism)原理与代码实战案例讲解

自注意力机制(Self-Attention Mechanism)原理与代码实战案例讲解 作者:禅与计算机程序设计艺术 / Zen and the Art of Computer Programming 1. 背景介绍

AI天才研究院 1289

LeetCode //C - 410. Split Array Largest Sum

【代码】LeetCode //C - 410. Split Array Largest Sum。

Made in Code 1163

IEM模型,地表粗糙度模拟

利用微波反演土壤水分时候,利用IEM模型可以实现对随机地表的模拟

SAP B1中文培訓教材_SDK

公司内部培训资料,对开发工具 ,DIAPI, UIAPI,Add-ON有详细解说

【Leetcode】410. Split Array Largest Sum

题目链接:https://leetcode.com/contest/7/problems/split-array-largest-sum/] 题目: Given an array which consists of non-negative integers and an integer m, you can split the array into m non-empty con

业精于勤,荒于嬉 4919

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")

分割数组的最大值(Split Array Largest Sum)java_leetcode 410

分割数组的最大值(Split Array Largest Sum)java_leetcode 410 题干 给定一个非负整数数组和一个整数 m,你需要将这个数组分成 m 个非空的连续子数组。设计一个算法使得这 m 个子数组各自和的最大值最小。 注意: 数组长度 n 满足以下条件: 1 ≤ n ≤ 1000 1 ≤ m ≤ min(50, n) 示例: 输入: nums = [7,2,5,10...

下雨了_简的博客 948

计算机控制技术及应用

《计算机控制技术及应用》通过总结作者的科研成果和吸收国内外的先进理论、方法和技术,以计算机控制系统的网络化、开放化、智能化和集成化发展趋势为主线索,主要介绍计算机控制系统的基本概念和计算机控制系统的典型形式,计算机控制系统的数学描述与基本分析方法,以及数字PID控制器、最少拍控制系统、达林算法、状态变量设计法等设计方法及其各种改进算法。重点突出计算机控制系统输入输出通道接口的设计方法和典型控制系统的设计案例与实现方法等。

Leetcode 410.分割数组的最大值(Split Array Largest Sum)

Leetcode 410.分割数组的最大值 1 题目描述(Leetcode题目链接)   给定一个非负整数数组和一个整数 m,你需要将这个数组分成 m 个非空的连续子数组。设计一个算法使得这 m 个子数组各自和的最大值最小。 注意: 数组长度 n 满足以下条件: 1 ≤ n ≤ 1000 1 ≤ m ≤ min(50, n) 输入: nums = [7,2,5,10,8] m = 2 输出: 18 解释: 一共有四种方法将nums分割为2个子数组。 其中最好的方式是将其分为[7,2,5] 和 [10

qq_39378221的博客 277

Leetcode 410. Split Array Largest Sum

dp[i][j] 表示将数组中前j个数字分成i组所能得到的最小的各个子数组中最大值 i的范围是[1, j] 计算dp[i][j]时,对于所有1<=k<=j-1: 最后一个分组为nums[k+1]…nums[j]时,此时各个子数组中最大值为max(sum(nums[k+1:j+1]), dp[i-1][k]) 遍历k得到最小的dp[i][j] 最终返回 dp[m][n] 即可 class Solution { public: int spli...

I_ren的博客 336

leetcode 410. Split Array Largest Sum

https://leetcode-cn.com/problems/split-array-largest-sum/ 有两种思路。一种是dp。对于 [0,j] 之间的元素要分成 i组。则可以对于所有 0<=k<j,都可以将[0,k]分成 i-1组。[k,j]分生一组。看两部分最大值哪个大。取大的即可。复杂度是O(mn^2) class Solution { public: ...

汤瑞的博客 213

[LeetCode] 410. Split Array Largest Sum

题目链接: https://leetcode.com/problems/split-array-largest-sum/description/ Description Given an array which consists of non-negative integers and an integer m, you can split the array into m non-em

从入门到扑街 893

[LeetCode 410] Split Array Largest Sum

Given an array which consists of non-negative integers and an integerm, you can split the array intomnon-empty continuous subarrays. Write an algorithm to minimize the largest sum among thesemsub...

ExcitedZhang的博客 198

[leetcode]410. Split Array Largest Sum 分割数组的最大值

题目链接:https://leetcode.com/problems/split-array-largest-sum/#/description Given an array which consists of non-negative integers and an integerm, you can split the array intomnon-empty continuo

xiaocong1990的博客 471

【LeetCode 410】 Split Array Largest Sum

题目描述 Given an array which consists of non-negative integers and an integer m, you can split the array into m non-empty continuous subarrays. Write an algorithm to minimize the largest sum among these ...

iCode_girl的博客 215

LeetCode 410 - Split Array Largest Sum

410. Split Array Largest Sum问题描述 Given an array which consists of non-negative integers and an integer m, you can split the array into m non-empty continuous subarrays. Write an algorithm to minimize

Gregzeng的博客 585

LeetCode 410. Split Array Largest Sum

410. Split Array Largest SumGiven an array which consists of non-negative integers and an integer m, you can split the array into m non-empty continuous subarrays. Write an algorithm to minimize the la

mingjc的博客 546

[leetcode] 410. Split Array Largest Sum

Description Given an array which consists of non-negative integers and an integer m, you can split the array into m non-empty continuous subarrays. Write an algorithm to minimize the largest sum among...

Learning from the mistakes 249
上一篇: Otsu算法实现(matlab)
下一篇: leetcode 132.Palindrome Partitioning II (回文子串分割2)
蓝羽飞鸟
博客等级 码龄8年 1万+粉丝 727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

打赏作者

蓝羽飞鸟

你的鼓励将是我创作的最大动力

¥1 ¥2 ¥4 ¥6 ¥10 ¥20
扫码支付:¥1
获取中
扫码支付

您的余额不足,请更换扫码支付或充值

打赏作者

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