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7.5 &nbsp; AVL 树 *
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7.6 &nbsp; 小结
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第 8 章 &nbsp; 堆
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第 8 章 &nbsp; 堆
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8.1 &nbsp; 堆
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8.2 &nbsp; 建堆操作
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8.3 &nbsp; Top-k 问题
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第 9 章 &nbsp; 图
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第 9 章 &nbsp; 图
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9.1 &nbsp; 图
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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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第 10 章 &nbsp; 搜索
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第 10 章 &nbsp; 搜索
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10.1 &nbsp; 二分查找
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10.2 &nbsp; 二分查找插入点
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10.3 &nbsp; 二分查找边界
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10.4 &nbsp; 哈希优化策略
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10.5 &nbsp; 重识搜索算法
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10.6 &nbsp; 小结
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第 11 章 &nbsp; 排序
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11.1 &nbsp; 排序算法
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11.2 &nbsp; 选择排序
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11.3 &nbsp; 冒泡排序
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11.4 &nbsp; 插入排序
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11.5 &nbsp; 快速排序
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11.6 &nbsp; 归并排序
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11.7 &nbsp; 堆排序
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11.8 &nbsp; 桶排序
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11.9 &nbsp; 计数排序
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11.10 &nbsp; 基数排序
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11.11 &nbsp; 小结
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第 12 章 &nbsp; 分治
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12.1 &nbsp; 分治算法
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12.2 &nbsp; 分治搜索策略
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12.3 &nbsp; 构建树问题
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12.4 &nbsp; 汉诺塔问题
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12.5 &nbsp; 小结
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第 13 章 &nbsp; 回溯
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第 13 章 &nbsp; 回溯
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13.1 &nbsp; 回溯算法
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13.2 &nbsp; 全排列问题
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13.3 &nbsp; 子集和问题
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13.4 &nbsp; N 皇后问题
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13.5 &nbsp; 小结
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第 14 章 &nbsp; 动态规划
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14.1 &nbsp; 初探动态规划
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14.2 &nbsp; DP 问题特性
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14.3 &nbsp; DP 解题思路
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14.4 &nbsp; 0-1 背包问题
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14.5 &nbsp; 完全背包问题
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14.6 &nbsp; 编辑距离问题
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<h1 id="13">1.3 &nbsp; 小结<a class="headerlink" href="#13" title="Permanent link">&para;</a></h1>
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<li>算法在日常生活中无处不在，并不是遥不可及的高深知识。实际上，我们已经在不知不觉中学会了许多算法，用以解决生活中的大小问题。</li>
<li>查字典的原理与二分查找算法相一致。二分查找算法体现了分而治之的重要算法思想。</li>
<li>整理扑克的过程与插入排序算法非常类似。插入排序算法适合排序小型数据集。</li>
<li>货币找零的步骤本质上是贪心算法，每一步都采取当前看来最好的选择。</li>
<li>算法是在有限时间内解决特定问题的一组指令或操作步骤，而数据结构是计算机中组织和存储数据的方式。</li>
<li>数据结构与算法紧密相连。数据结构是算法的基石，而算法是数据结构发挥作用的舞台。</li>
<li>我们可以将数据结构与算法类比为拼装积木，积木代表数据，积木的形状和连接方式等代表数据结构，拼装积木的步骤则对应算法。</li>
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<h3 id="1-q-a">1. &nbsp; Q &amp; A<a class="headerlink" href="#1-q-a" title="Permanent link">&para;</a></h3>
<p><strong>Q</strong>：作为一名程序员，我在日常工作中从未用算法解决过问题，常用算法都被编程语言封装好了，直接用就可以了；这是否意味着我们工作中的问题还没有到达需要算法的程度？</p>
<p>如果把具体的工作技能比作是武功的“招式”的话，那么基础科目应该更像是“内功”。</p>
<p>我认为学算法（以及其他基础科目）的意义不是在于在工作中从零实现它，而是基于学到的知识，在解决问题时能够作出专业的反应和判断，从而提升工作的整体质量。举一个简单例子，每种编程语言都内置了排序函数：</p>
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<li>如果我们没有学过数据结构与算法，那么给定任何数据，我们可能都塞给这个排序函数去做了。运行顺畅、性能不错，看上去并没有什么问题。</li>
<li>但如果学过算法，我们就会知道内置排序函数的时间复杂度是 <span class="arithmatex">\(O(n \log n)\)</span> ；而如果给定的数据是固定位数的整数（例如学号），那么我们就可以用效率更高的“基数排序”来做，将时间复杂度降为 <span class="arithmatex">\(O(nk)\)</span> ，其中 <span class="arithmatex">\(k\)</span> 为位数。当数据体量很大时，节省出来的运行时间就能创造较大价值（成本降低、体验变好等）。</li>
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<p>在工程领域中，大量问题是难以达到最优解的，许多问题只是被“差不多”地解决了。问题的难易程度一方面取决于问题本身的性质，另一方面也取决于观测问题的人的知识储备。人的知识越完备、经验越多，分析问题就会越深入，问题就能被解决得更优雅。</p>
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