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2.3. &nbsp; 空间复杂度
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2.4. &nbsp; 权衡时间与空间
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3.2. &nbsp; 数据结构分类
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4.1. &nbsp; 数组Array
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4.2. &nbsp; 链表Linked List
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4.3. &nbsp; 列表List
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5.2. &nbsp; 队列Queue
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5.3. &nbsp; 双向队列Deque
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6.1. &nbsp; 哈希表Hash Map
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6.2. &nbsp; 哈希冲突处理
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7. &nbsp; &nbsp; 二叉树
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7.1. &nbsp; 二叉树Binary Tree
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7.2. &nbsp; 二叉树遍历
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7.3. &nbsp; 二叉搜索树
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7.4. &nbsp; AVL 树 *
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8. &nbsp; &nbsp;
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8.1. &nbsp;Heap
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8.2. &nbsp; 建堆操作 *
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9.1. &nbsp;Graph
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9.2. &nbsp; 图基础操作
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9.3. &nbsp; 图的遍历
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10. &nbsp; &nbsp; 查找算法
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10. &nbsp; &nbsp; 查找算法
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10.1. &nbsp; 线性查找
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10.2. &nbsp; 二分查找
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10.3. &nbsp; 哈希查找
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11. &nbsp; &nbsp; 排序算法
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11. &nbsp; &nbsp; 排序算法
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11.1. &nbsp; 排序简介
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11.2. &nbsp; 冒泡排序
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11.3. &nbsp; 插入排序
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11.4. &nbsp; 快速排序
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11.5. &nbsp; 归并排序
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11.6. &nbsp; 小结
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0.1.1. &nbsp; 读者对象
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0.1.2. &nbsp; 内容结构
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复杂度分析
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数据结构
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算法
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0.1.3. &nbsp; 本书定位
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0.1.4. &nbsp; 本书特点
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0.1.5. &nbsp; 致谢
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<div class="md-content" data-md-component="content">
<article class="md-content__inner md-typeset">
<a href="https://github.com/krahets/hello-algo/tree/main/docs/chapter_preface/about_the_book.md" title="编辑此页" class="md-content__button md-icon">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M10 20H6V4h7v5h5v3.1l2-2V8l-6-6H6c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h4v-2m10.2-7c.1 0 .3.1.4.2l1.3 1.3c.2.2.2.6 0 .8l-1 1-2.1-2.1 1-1c.1-.1.2-.2.4-.2m0 3.9L14.1 23H12v-2.1l6.1-6.1 2.1 2.1Z"/></svg>
</a>
<h1 id="01">0.1. &nbsp; 关于本书<a class="headerlink" href="#01" title="Permanent link">&para;</a></h1>
<div class="admonition tip">
<p class="admonition-title">Tip</p>
<p>对于本章内容,建议通读<a href="https://www.hello-algo.com/chapter_preface/suggestions/">如何使用本书</a>,其余章节可根据需求选读。</p>
</div>
<p>五年前发生的一件事,成为了我职业生涯的重要转折点。当时的我在交大读研,对互联网求职一无所知,但仍然硬着头皮申请了 Microsoft 软件工程师实习。面试官让我在白板上写出“快速排序”代码,我畏畏缩缩地写了一个“冒泡排序”,并且还写错了<code>(ToT)</code> 。从面试官的表情上,我看到了一个大大的 "GG" 。</p>
<p>此次失利倒逼我开始刷算法题。我采用“扫雷游戏”式的学习方法,两眼一抹黑刷题,扫到不会的“雷”就通过查资料把它“排掉”,配合周期性总结,逐渐形成了数据结构与算法的知识图景。幸运地,我在秋招斩获了多家大厂的 Offer 。</p>
<p>回想自己当初在“扫雷式”刷题中被炸的满头包的痛苦,思考良久,<strong>我意识到一本“前期刷题必看”的读物可以使算法小白少走许多弯路</strong>。写作意愿滚滚袭来,那就动笔吧:</p>
<h4 align="center"> Hello算法 </h4>
<h2 id="011">0.1.1. &nbsp; 读者对象<a class="headerlink" href="#011" title="Permanent link">&para;</a></h2>
<p>如果您是「算法初学者」,完全没有接触过算法,或者已经有少量刷题,对数据结构与算法有朦胧的理解,在会与不会之间反复横跳,那么这本书就是为您而写!本书能够带来:</p>
<ul>
<li>带领你完整学习数据结构与算法,初步搭建知识框架,使刷题事半功倍;</li>
<li>提供写法标准整洁、可一键运行的配套代码,帮助你养成良好的编程习惯;</li>
</ul>
<p>如果您是「算法老手」,已经积累一定刷题量,接触过大多数题型,那么本书内容对你来说可能稍显基础,但仍能够带来以下价值:</p>
<ul>
<li>本书篇幅不长,并且包含较多对比性和总结性的内容,可以帮助你回顾与梳理算法知识体系;</li>
<li>源代码实现了各种经典数据结构和算法,可以作为“刷题工具库”或“算法实现字典”来使用;</li>
</ul>
<p>如果您是「算法大佬」,请受我膜拜!希望您可以抽时间提出意见建议,或者<a href="https://www.hello-algo.com/chapter_preface/contribution/">一起参与创作</a>,帮助各位同学获取更好的学习内容,感谢!</p>
<div class="admonition success">
<p class="admonition-title">前置条件</p>
<p>您需要至少具备任一语言的编程基础,能够阅读和编写简单代码。</p>
</div>
<h2 id="012">0.1.2. &nbsp; 内容结构<a class="headerlink" href="#012" title="Permanent link">&para;</a></h2>
<p>本书主要内容分为复杂度分析、数据结构、算法三个部分。</p>
<p><img alt="Hello 算法内容结构" src="../about_the_book.assets/hello_algo_mindmap.png" /></p>
<p align="center"> Fig. Hello 算法内容结构 </p>
<h3 id="_1">复杂度分析<a class="headerlink" href="#_1" title="Permanent link">&para;</a></h3>
<p>首先介绍数据结构与算法的评价维度、算法效率的评估方法,引出了计算复杂度概念。</p>
<p>接下来,从 <strong>函数渐近上界</strong> 入手,分别介绍了 <strong>时间复杂度</strong><strong>空间复杂度</strong>,包括推算方法、常见类型、示例等。同时,剖析了 <strong>最差、最佳、平均</strong> 时间复杂度的联系与区别。</p>
<h3 id="_2">数据结构<a class="headerlink" href="#_2" title="Permanent link">&para;</a></h3>
<p>首先介绍了常用的 <strong>基本数据类型</strong> 、以及它们是如何在内存中存储的。</p>
<p>接下来,介绍了两种 <strong>数据结构分类方法</strong>,包括逻辑结构与物理结构。</p>
<p>后续展开介绍了 <strong>数组、链表、栈、队列、散列表、树、堆、图</strong> 等数据结构,关心以下内容:</p>
<ul>
<li>基本定义:数据结构的设计来源、存在意义;</li>
<li>主要特点:在各项数据操作中的优势、劣势;</li>
<li>常用操作:例如访问、更新、插入、删除、遍历、搜索等;</li>
<li>常见类型:在算法题或工程实际中,经常碰到的数据结构类型;</li>
<li>典型应用:此数据结构经常搭配哪些算法使用;</li>
<li>实现方法:对于重要的数据结构,将给出完整的实现示例;</li>
</ul>
<h3 id="_3">算法<a class="headerlink" href="#_3" title="Permanent link">&para;</a></h3>
<p>包括 <strong>查找算法、排序算法、搜索与回溯、动态规划、分治算法</strong>,内容包括:</p>
<ul>
<li>基本定义:算法的设计思想;</li>
<li>主要特点:使用前置条件、优势和劣势;</li>
<li>算法效率:最差和平均时间复杂度、空间复杂度;</li>
<li>实现方法:完整的算法实现,以及优化措施;</li>
<li>示例题目:结合例题加深理解;</li>
</ul>
<h2 id="013">0.1.3. &nbsp; 本书定位<a class="headerlink" href="#013" title="Permanent link">&para;</a></h2>
<p>总体上看,我认为可将学习数据结构与算法的过程分为三个阶段。</p>
<ul>
<li><strong>第一阶段,算法入门</strong>。熟悉各种数据结构的特点、用法,学习各种算法的原理、流程、用途、效率等。</li>
<li><strong>第二阶段,刷算法题</strong>。可以先从热门题单开刷,推荐<a href="https://leetcode.cn/problem-list/xb9nqhhg/">剑指 Offer</a><a href="https://leetcode.cn/problem-list/2cktkvj/">LeetCode Hot 100</a>,先积累至少 100 道题量,熟悉大多数的算法问题。刚开始刷题时,“遗忘”是最大的困扰点,但这是很正常的,请不要担心。学习中有一种概念叫“周期性回顾”,同一道题隔段时间做一次,在重复 3 轮以上后,往往就能牢记于心了。</li>
<li><strong>第三阶段,搭建知识体系</strong>。在学习方面,可以阅读算法专栏文章、解题框架、算法教材,不断地丰富知识体系。在刷题方面,可以开始采用进阶刷题方案,例如按专题分类、一题多解、一解多题等,相应刷题计划与心得可以在社区中找到,在此不做赘述。</li>
</ul>
<p>根据观察,很多同学都是从“第二阶段”开始学习算法的。而作为入门教程,<strong>本书内容主要对应“第一阶段”</strong>,致力于帮助读者更高效地开展第二、三阶段的学习。</p>
<p><img alt="算法学习路线" src="../suggestions.assets/learning_route.png" /></p>
<p align="center"> Fig. 算法学习路线 </p>
<h2 id="014">0.1.4. &nbsp; 本书特点<a class="headerlink" href="#014" title="Permanent link">&para;</a></h2>
<p><strong>以实践为主</strong>。我们知道,学习英语期间光啃书本是远远不够的,需要多听、多说、多写,在实践中培养语感、积累经验。编程语言也是一门语言,因此学习方法也应是类似的,需要多看优秀代码、多敲键盘、多思考代码逻辑。</p>
<p>本书的理论部分占少量篇幅,主要分为两类:一是基础且必要的概念知识,以培养读者对于算法的感性认识;二是重要的分类、对比或总结,这是为了帮助你站在更高视角俯瞰各个知识点,形成连点成面的效果。</p>
<p>实践部分主要由示例和代码组成。代码配有简要注释,复杂示例会尽可能地使用视觉化的形式呈现。我强烈建议读者对照着代码自己敲一遍,如果时间有限,也至少逐行读、复制并运行一遍,配合着讲解将代码吃透。</p>
<p><strong>视觉化学习</strong>。信息时代以来,视觉化的脚步从未停止。媒体形式经历了文字短信、图文 Email 、动图、短(长)视频、交互式 Web 、3D 游戏等演变过程信息的视觉化程度越来越高、愈加符合人类感官、信息传播效率大大提升。科技界也在向视觉化迈进iPhone 就是一个典型例子,其相对于传统手机是高度视觉化的,包含精心设计的字体、主题配色、交互动画等。</p>
<p>近两年,短视频成为最受欢迎的信息媒介,可以在短时间内将高密度的信息“灌”给我们,有着极其舒适的观看体验。阅读则不然,读者与书本之间天然存在一种“疏离感”,我们看书会累、会走神、会停下来想其他事、会划下喜欢的句子、会思考某一片段的含义,这种疏离感给了读者与书本之间对话的可能,拓宽了想象空间。</p>
<p>本书作为一本入门教材,希望可以保有书本的“慢节奏”,但也会避免与读者产生过多“疏离感”,而是努力将知识完整清晰地推送到你聪明的小脑袋瓜中。我将采用视觉化的方式(例如配图、动画),尽我可能清晰易懂地讲解复杂概念和抽象示例。</p>
<p><strong>内容精简化</strong>。大多数的经典教科书,会把每个主题都讲的很透彻。虽然透彻性正是其获得读者青睐的原因,但对于想要快速入门的初学者来说,这些教材的实用性不足。本书会避免引入非必要的概念、名词、定义等,也避免展开不必要的理论分析,毕竟这不是一本真正意义上的教材,主要任务是尽快地带领读者入门。</p>
<p>引入一些生活案例或趣味内容,非常适合作为知识点的引子或者解释的补充,但当融入过多额外元素时,内容会稍显冗长,也许反而使读者容易迷失、抓不住重点,这也是本书需要避免的。</p>
<p>敲代码如同写字,“美”是统一的追求。本书力求美观的代码,保证规范的变量命名、统一的空格与换行、对齐的缩进、整齐的注释等。</p>
<h2 id="015">0.1.5. &nbsp; 致谢<a class="headerlink" href="#015" title="Permanent link">&para;</a></h2>
<p>本书的成书过程中,我获得了许多人的帮助,包括但不限于:</p>
<ul>
<li>感谢我的女朋友泡泡担任本书的首位读者,从算法小白的视角为本书的写作提出了许多建议,使这本书更加适合算法初学者来阅读。</li>
<li>感谢腾宝、琦宝、飞宝为本书起了个响当当的名字,好听又有梗,直接唤起我最初敲下第一行代码 "Hello, World!" 的回忆。</li>
<li>感谢我的导师李博,在小酌畅谈时您告诉我“觉得应该做就去做”,坚定了我写这本书的决心。</li>
<li>感谢苏潼为本书设计了封面和 LOGO ,我有些强迫症,前后多次修改,谢谢你的耐心。</li>
<li>感谢 @squidfunk 给出的写作排版建议,以及优秀开源项目 <a href="https://github.com/squidfunk/mkdocs-material/tree/master">Material-for-MkDocs</a></li>
</ul>
<p>本书鼓励“手脑并用”的学习方式,在这点上受到了《动手学深度学习》很大影响,也在此向各位同学强烈推荐这本著作,包括<a href="https://github.com/d2l-ai/d2l-zh">中文版</a><a href="https://github.com/d2l-ai/d2l-en">英文版</a><a href="https://space.bilibili.com/1567748478">李沐老师 bilibili 主页</a></p>
<p>在写作过程中,我阅读了许多数据结构与算法的教材与文章,这些著作为本书作出了很好的榜样,保证了本书内容的正确性与质量,感谢前辈们的精彩创作!</p>
<p>感谢父母,你们一贯的支持与鼓励给了我自由度来做这些有趣的事。</p>
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