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<h1 id="34">3.4 &nbsp; 字符编码 *<a class="headerlink" href="#34" title="Permanent link">&para;</a></h1>
<p>在计算机中,所有数据都是以二进制数的形式存储的,字符 <code>char</code> 也不例外。为了表示字符,我们需要建立一套“字符集”,规定每个字符和二进制数之间的一一对应关系。有了字符集之后,计算机就可以通过查表完成二进制数到字符的转换。</p>
<h2 id="341-ascii">3.4.1 &nbsp; ASCII 字符集<a class="headerlink" href="#341-ascii" title="Permanent link">&para;</a></h2>
<p><u>ASCII(码)</u>是最早出现的字符集,其全称为 American Standard Code for Information Interchange美国标准信息交换代码。它使用 7 位二进制数(一个字节的低 7 位)表示一个字符,最多能够表示 128 个不同的字符。如图 3-6 所示ASCII 码包括英文字母的大小写、数字 0 ~ 9、一些标点符号以及一些控制字符如换行符和制表符</p>
<p><u>ASCII</u>是最早出现的字符集,其全称为 American Standard Code for Information Interchange美国标准信息交换代码。它使用 7 位二进制数(一个字节的低 7 位)表示一个字符,最多能够表示 128 个不同的字符。如图 3-6 所示ASCII 码包括英文字母的大小写、数字 0 ~ 9、一些标点符号以及一些控制字符如换行符和制表符</p>
<p><a class="glightbox" href="../character_encoding.assets/ascii_table.png" data-type="image" data-width="100%" data-height="auto" data-desc-position="bottom"><img alt="ASCII 码" class="animation-figure" src="../character_encoding.assets/ascii_table.png" /></a></p>
<p align="center"> 图 3-6 &nbsp; ASCII 码 </p>

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<p>与回溯算法一样,动态规划也使用“状态”概念来表示问题求解的特定阶段,每个状态都对应一个子问题以及相应的局部最优解。例如,爬楼梯问题的状态定义为当前所在楼梯阶数 <span class="arithmatex">\(i\)</span></p>
<p>根据以上内容,我们可以总结出动态规划的常用术语。</p>
<ul>
<li>将数组 <code>dp</code> 称为<u><span class="arithmatex">\(dp\)</span>(表)</u><span class="arithmatex">\(dp[i]\)</span> 表示状态 <span class="arithmatex">\(i\)</span> 对应子问题的解。</li>
<li>将数组 <code>dp</code> 称为<u>dp 表</u><span class="arithmatex">\(dp[i]\)</span> 表示状态 <span class="arithmatex">\(i\)</span> 对应子问题的解。</li>
<li>将最小子问题对应的状态(第 <span class="arithmatex">\(1\)</span> 阶和第 <span class="arithmatex">\(2\)</span> 阶楼梯)称为<u>初始状态</u></li>
<li>将递推公式 <span class="arithmatex">\(dp[i] = dp[i-1] + dp[i-2]\)</span> 称为<u>状态转移方程</u></li>
</ul>

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<p>计算机的出现给世界带来了巨大变革,它凭借高速的计算能力和出色的可编程性,成为了执行算法与处理数据的理想媒介。无论是电子游戏的逼真画面、自动驾驶的智能决策,还是 AlphaGo 的精彩棋局、ChatGPT 的自然交互,这些应用都是算法在计算机上的精妙演绎。</p>
<p>事实上,在计算机问世之前,算法和数据结构就已经存在于世界的各个角落。早期的算法相对简单,例如古代的计数方法和工具制作步骤等。随着文明的进步,算法逐渐变得更加精细和复杂。从巧夺天工的匠人技艺、到解放生产力的工业产品、再到宇宙运行的科学规律,几乎每一件平凡或令人惊叹的事物背后,都隐藏着精妙的算法思想。</p>
<p>同样,数据结构无处不在:大到社会网络,小到地铁线路,许多系统都可以建模为“图”;大到一个国家,小到一个家庭,社会的主要组织形式呈现出“树”的特征;冬天的衣服就像“栈”,最先穿上的最后才能脱下;羽毛球筒则如同“队列”,一端放入、另一端取出;字典就像一个“哈希表”,能够快速查找目标词条。</p>
<p>本书旨在通过清晰易懂的动画图和可运行的代码示例,使读者理解算法和数据结构的核心概念,并能够通过编程来实现它们。在此基础上,本书致力于揭示算法在复杂世界中的生动体现,展现算法之美。希望本书能够帮助到你!</p>
<p>本书旨在通过清晰易懂的动画图和可运行的代码示例,使读者理解算法和数据结构的核心概念,并能够通过编程来实现它们。在此基础上,本书致力于揭示算法在复杂世界中的生动体现,展现算法之美。希望本书能够帮助到你!</p>
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<p><a class="glightbox" href="../avl_tree.assets/avltree_degradation_from_inserting_node.png" data-type="image" data-width="100%" data-height="auto" data-desc-position="bottom"><img alt="AVL 树在插入节点后发生退化" class="animation-figure" src="../avl_tree.assets/avltree_degradation_from_inserting_node.png" /></a></p>
<p align="center"> 图 7-25 &nbsp; AVL 树在插入节点后发生退化 </p>
<p>1962 年 G. M. Adelson-Velsky 和 E. M. Landis 在论文“An algorithm for the organization of information”中提出了<u>AVL(树)</u>。论文中详细描述了一系列操作确保在持续添加和删除节点后AVL 树不会退化,从而使得各种操作的时间复杂度保持在 <span class="arithmatex">\(O(\log n)\)</span> 级别。换句话说在需要频繁进行增删查改操作的场景中AVL 树能始终保持高效的数据操作性能,具有很好的应用价值。</p>
<p>1962 年 G. M. Adelson-Velsky 和 E. M. Landis 在论文“An algorithm for the organization of information”中提出了<u>AVL</u>。论文中详细描述了一系列操作确保在持续添加和删除节点后AVL 树不会退化,从而使得各种操作的时间复杂度保持在 <span class="arithmatex">\(O(\log n)\)</span> 级别。换句话说在需要频繁进行增删查改操作的场景中AVL 树能始终保持高效的数据操作性能,具有很好的应用价值。</p>
<h2 id="751-avl">7.5.1 &nbsp; AVL 树常见术语<a class="headerlink" href="#751-avl" title="Permanent link">&para;</a></h2>
<p>AVL 树既是二叉搜索树,也是平衡二叉树,同时满足这两类二叉树的所有性质,因此是一种<u>平衡二叉搜索树balanced binary search tree</u></p>
<h3 id="1">1. &nbsp; 节点高度<a class="headerlink" href="#1" title="Permanent link">&para;</a></h3>

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