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7.3. &nbsp; 二叉树数组表示
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7.4. &nbsp; 二叉搜索树
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9. &nbsp;
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9.1. &nbsp;
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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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10.1. &nbsp; 二分查找
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10.4.1. &nbsp; 暴力搜索
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<a href="https://github.com/krahets/hello-algo/tree/main/docs/chapter_searching/searching_algorithm_revisited.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>
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<h1 id="104">10.4. &nbsp; 重识搜索算法<a class="headerlink" href="#104" title="Permanent link">&para;</a></h1>
<p>「搜索算法 Searching Algorithm」用于在数据结构例如数组、链表、树或图中搜索一个或一组满足特定条件的元素。</p>
<p>根据实现思路,搜索算法总体可分为两种:</p>
<ul>
<li><strong>通过遍历数据结构来定位目标元素</strong>,例如数组、链表、树和图的遍历等。</li>
<li><strong>利用数据组织结构或数据包含的先验信息,实现高效元素查找</strong>,例如二分查找、哈希查找和二叉搜索树查找等。</li>
</ul>
<p>不难发现,这些知识点都已在前面的章节中介绍过,因此搜索算法对于我们来说并不陌生。在本节中,我们将从更加系统的视角切入,重新审视搜索算法。</p>
<h2 id="1041">10.4.1. &nbsp; 暴力搜索<a class="headerlink" href="#1041" title="Permanent link">&para;</a></h2>
<p>暴力搜索通过遍历数据结构的每个元素来定位目标元素。</p>
<ul>
<li>「线性搜索」适用于数组和链表等线性数据结构。它从数据结构的一端开始,逐个访问元素,直到找到目标元素或到达另一端仍没有找到目标元素为止。</li>
<li>「广度优先搜索」和「深度优先搜索」是图和树的两种遍历策略。广度优先搜索从初始节点开始逐层搜索,由近及远地访问各个节点。深度优先搜索是从初始节点开始,沿着一条路径走到头为止,再回溯并尝试其他路径,直到遍历完整个数据结构。</li>
</ul>
<p>暴力搜索的优点是简单且通用性好,<strong>无需对数据做预处理和借助额外的数据结构</strong></p>
<p>然而,<strong>此类算法的时间复杂度为 <span class="arithmatex">\(O(n)\)</span></strong> ,其中 <span class="arithmatex">\(n\)</span> 为元素数量,因此在数据量较大的情况下性能较差。</p>
<h2 id="1042">10.4.2. &nbsp; 自适应搜索<a class="headerlink" href="#1042" title="Permanent link">&para;</a></h2>
<p>自适应搜索利用数据的特有属性(例如有序性)来优化搜索过程,从而更高效地定位目标元素。</p>
<ul>
<li>「二分查找」利用数据的有序性实现高效查找,仅适用于数组。</li>
<li>「哈希查找」利用哈希表将搜索数据和目标数据建立为键值对映射,从而实现查询操作。</li>
<li>「树查找」在特定的树结构(例如二叉搜索树)中,基于比较节点值来快速排除节点,从而定位目标元素。</li>
</ul>
<p>此类算法的优点是效率高,<strong>时间复杂度可达到 <span class="arithmatex">\(O(\log n)\)</span> 甚至 <span class="arithmatex">\(O(1)\)</span></strong></p>
<p>然而,<strong>使用这些算法往往需要对数据进行预处理</strong>。例如,二分查找需要预先对数组进行排序,哈希查找和树查找都需要借助额外的数据结构,维护这些数据结构也需要额外的时间和空间开支。</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>自适应搜索算法常被称为查找算法,<strong>主要关注在特定数据结构中快速检索目标元素</strong></p>
</div>
<h2 id="1043">10.4.3. &nbsp; 搜索方法选取<a class="headerlink" href="#1043" title="Permanent link">&para;</a></h2>
<p>给定大小为 <span class="arithmatex">\(n\)</span> 的一组数据,我们可以使用线性搜索、二分查找、树查找、哈希查找等多种方法在该数据中搜索目标元素。各个方法的工作原理如下图所示。</p>
<p><img alt="多种搜索策略" src="../searching_algorithm_revisited.assets/searching_algorithms.png" /></p>
<p align="center"> Fig. 多种搜索策略 </p>
<p>上述几种方法的操作效率与特性如下表所示。</p>
<div class="center-table">
<table>
<thead>
<tr>
<th></th>
<th>线性搜索</th>
<th>二分查找</th>
<th>树查找</th>
<th>哈希查找</th>
</tr>
</thead>
<tbody>
<tr>
<td>查找元素</td>
<td><span class="arithmatex">\(O(n)\)</span></td>
<td><span class="arithmatex">\(O(\log n)\)</span></td>
<td><span class="arithmatex">\(O(\log n)\)</span></td>
<td><span class="arithmatex">\(O(1)\)</span></td>
</tr>
<tr>
<td>插入元素</td>
<td><span class="arithmatex">\(O(1)\)</span></td>
<td><span class="arithmatex">\(O(n)\)</span></td>
<td><span class="arithmatex">\(O(\log n)\)</span></td>
<td><span class="arithmatex">\(O(1)\)</span></td>
</tr>
<tr>
<td>删除元素</td>
<td><span class="arithmatex">\(O(n)\)</span></td>
<td><span class="arithmatex">\(O(n)\)</span></td>
<td><span class="arithmatex">\(O(\log n)\)</span></td>
<td><span class="arithmatex">\(O(1)\)</span></td>
</tr>
<tr>
<td>额外空间</td>
<td><span class="arithmatex">\(O(1)\)</span></td>
<td><span class="arithmatex">\(O(1)\)</span></td>
<td><span class="arithmatex">\(O(n)\)</span></td>
<td><span class="arithmatex">\(O(n)\)</span></td>
</tr>
<tr>
<td>数据预处理</td>
<td>/</td>
<td>排序 <span class="arithmatex">\(O(n \log n)\)</span></td>
<td>建树 <span class="arithmatex">\(O(n \log n)\)</span></td>
<td>建哈希表 <span class="arithmatex">\(O(n)\)</span></td>
</tr>
<tr>
<td>数据是否有序</td>
<td>无序</td>
<td>有序</td>
<td>有序</td>
<td>无序</td>
</tr>
</tbody>
</table>
</div>
<p>除了以上表格内容,搜索算法的选择还取决于数据体量、搜索性能要求、数据查询与更新频率等。</p>
<p><strong>线性搜索</strong></p>
<ul>
<li>通用性较好,无需任何数据预处理操作。假如我们仅需查询一次数据,那么其他三种方法的数据预处理的时间比线性搜索的时间还要更长。</li>
<li>适用于体量较小的数据,此情况下时间复杂度对效率影响较小。</li>
<li>适用于数据更新频率较高的场景,因为该方法不需要对数据进行任何额外维护。</li>
</ul>
<p><strong>二分查找</strong></p>
<ul>
<li>适用于大数据量的情况,效率表现稳定,最差时间复杂度为 <span class="arithmatex">\(O(\log n)\)</span></li>
<li>数据量不能过大,因为存储数组需要连续的内存空间。</li>
<li>不适用于高频增删数据的场景,因为维护有序数组的开销较大。</li>
</ul>
<p><strong>哈希查找</strong></p>
<ul>
<li>适合对查询性能要求很高的场景,平均时间复杂度为 <span class="arithmatex">\(O(1)\)</span></li>
<li>不适合需要有序数据或范围查找的场景,因为哈希表无法维护数据的有序性。</li>
<li>对哈希函数和哈希冲突处理策略的依赖性较高,具有较大的性能劣化风险。</li>
<li>不适合数据量过大的情况,因为哈希表需要额外空间来最大程度地减少冲突,从而提供良好的查询性能。</li>
</ul>
<p><strong>树查找</strong></p>
<ul>
<li>适用于海量数据,因为树节点在内存中是离散存储的。</li>
<li>适合需要维护有序数据或范围查找的场景。</li>
<li>在持续增删节点的过程中,二叉搜索树可能产生倾斜,时间复杂度劣化至 <span class="arithmatex">\(O(n)\)</span></li>
<li>若使用 AVL 树或红黑树,则各项操作可在 <span class="arithmatex">\(O(\log n)\)</span> 效率下稳定运行,但维护树平衡的操作会增加额外开销。</li>
</ul>
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