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@@ -1687,6 +1687,8 @@
<li>「指令空间」用于保存编译后的程序指令，<strong>在实际统计中一般忽略不计</strong>。</li>
</ul>
<p><img alt="算法使用的相关空间" src="../space_complexity.assets/space_types.png" /></p>
<p align="center"> Fig. 算法使用的相关空间 </p>
<div class="tabbed-set tabbed-alternate" data-tabs="1:10"><input checked="checked" id="__tabbed_1_1" name="__tabbed_1" type="radio" /><input id="__tabbed_1_2" name="__tabbed_1" type="radio" /><input id="__tabbed_1_3" name="__tabbed_1" type="radio" /><input id="__tabbed_1_4" name="__tabbed_1" type="radio" /><input id="__tabbed_1_5" name="__tabbed_1" type="radio" /><input id="__tabbed_1_6" name="__tabbed_1" type="radio" /><input id="__tabbed_1_7" name="__tabbed_1" type="radio" /><input id="__tabbed_1_8" name="__tabbed_1" type="radio" /><input id="__tabbed_1_9" name="__tabbed_1" type="radio" /><input id="__tabbed_1_10" name="__tabbed_1" type="radio" /><div class="tabbed-labels"><label for="__tabbed_1_1">Java</label><label for="__tabbed_1_2">C++</label><label for="__tabbed_1_3">Python</label><label for="__tabbed_1_4">Go</label><label for="__tabbed_1_5">JavaScript</label><label for="__tabbed_1_6">TypeScript</label><label for="__tabbed_1_7">C</label><label for="__tabbed_1_8">C#</label><label for="__tabbed_1_9">Swift</label><label for="__tabbed_1_10">Zig</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
@@ -2171,6 +2173,8 @@ O(1) &lt; O(\log n) &lt; O(n) &lt; O(n^2) &lt; O(2^n) \newline
\end{aligned}
\]</div>
<p><img alt="空间复杂度的常见类型" src="../space_complexity.assets/space_complexity_common_types.png" /></p>
<p align="center"> Fig. 空间复杂度的常见类型 </p>
<div class="admonition tip">
<p class="admonition-title">Tip</p>
<p>部分示例代码需要一些前置知识，包括数组、链表、二叉树、递归算法等。如果遇到看不懂的地方无需担心，可以在学习完后面章节后再来复习，现阶段先聚焦在理解空间复杂度含义和推算方法上。</p>
@@ -2629,6 +2633,8 @@ O(1) &lt; O(\log n) &lt; O(n) &lt; O(n^2) &lt; O(2^n) \newline
</div>
</div>
<p><img alt="递归函数产生的线性阶空间复杂度" src="../space_complexity.assets/space_complexity_recursive_linear.png" /></p>
<p align="center"> Fig. 递归函数产生的线性阶空间复杂度 </p>
<h3 id="on2">平方阶 <span class="arithmatex">\(O(n^2)\)</span><a class="headerlink" href="#on2" title="Permanent link">&para;</a></h3>
<p>平方阶常见于元素数量与 <span class="arithmatex">\(n\)</span> 成平方关系的矩阵、图。</p>
<div class="tabbed-set tabbed-alternate" data-tabs="7:10"><input checked="checked" id="__tabbed_7_1" name="__tabbed_7" type="radio" /><input id="__tabbed_7_2" name="__tabbed_7" type="radio" /><input id="__tabbed_7_3" name="__tabbed_7" type="radio" /><input id="__tabbed_7_4" name="__tabbed_7" type="radio" /><input id="__tabbed_7_5" name="__tabbed_7" type="radio" /><input id="__tabbed_7_6" name="__tabbed_7" type="radio" /><input id="__tabbed_7_7" name="__tabbed_7" type="radio" /><input id="__tabbed_7_8" name="__tabbed_7" type="radio" /><input id="__tabbed_7_9" name="__tabbed_7" type="radio" /><input id="__tabbed_7_10" name="__tabbed_7" type="radio" /><div class="tabbed-labels"><label for="__tabbed_7_1">Java</label><label for="__tabbed_7_2">C++</label><label for="__tabbed_7_3">Python</label><label for="__tabbed_7_4">Go</label><label for="__tabbed_7_5">JavaScript</label><label for="__tabbed_7_6">TypeScript</label><label for="__tabbed_7_7">C</label><label for="__tabbed_7_8">C#</label><label for="__tabbed_7_9">Swift</label><label for="__tabbed_7_10">Zig</label></div>
@@ -2877,6 +2883,8 @@ O(1) &lt; O(\log n) &lt; O(n) &lt; O(n^2) &lt; O(2^n) \newline
</div>
</div>
<p><img alt="递归函数产生的平方阶空间复杂度" src="../space_complexity.assets/space_complexity_recursive_quadratic.png" /></p>
<p align="center"> Fig. 递归函数产生的平方阶空间复杂度 </p>
<h3 id="o2n">指数阶 <span class="arithmatex">\(O(2^n)\)</span><a class="headerlink" href="#o2n" title="Permanent link">&para;</a></h3>
<p>指数阶常见于二叉树。高度为 <span class="arithmatex">\(n\)</span> 的「满二叉树」的结点数量为 <span class="arithmatex">\(2^n - 1\)</span> ，使用 <span class="arithmatex">\(O(2^n)\)</span> 空间。</p>
<div class="tabbed-set tabbed-alternate" data-tabs="9:10"><input checked="checked" id="__tabbed_9_1" name="__tabbed_9" type="radio" /><input id="__tabbed_9_2" name="__tabbed_9" type="radio" /><input id="__tabbed_9_3" name="__tabbed_9" type="radio" /><input id="__tabbed_9_4" name="__tabbed_9" type="radio" /><input id="__tabbed_9_5" name="__tabbed_9" type="radio" /><input id="__tabbed_9_6" name="__tabbed_9" type="radio" /><input id="__tabbed_9_7" name="__tabbed_9" type="radio" /><input id="__tabbed_9_8" name="__tabbed_9" type="radio" /><input id="__tabbed_9_9" name="__tabbed_9" type="radio" /><input id="__tabbed_9_10" name="__tabbed_9" type="radio" /><div class="tabbed-labels"><label for="__tabbed_9_1">Java</label><label for="__tabbed_9_2">C++</label><label for="__tabbed_9_3">Python</label><label for="__tabbed_9_4">Go</label><label for="__tabbed_9_5">JavaScript</label><label for="__tabbed_9_6">TypeScript</label><label for="__tabbed_9_7">C</label><label for="__tabbed_9_8">C#</label><label for="__tabbed_9_9">Swift</label><label for="__tabbed_9_10">Zig</label></div>
@@ -2992,6 +3000,8 @@ O(1) &lt; O(\log n) &lt; O(n) &lt; O(n^2) &lt; O(2^n) \newline
</div>
</div>
<p><img alt="满二叉树产生的指数阶空间复杂度" src="../space_complexity.assets/space_complexity_exponential.png" /></p>
<p align="center"> Fig. 满二叉树产生的指数阶空间复杂度 </p>
<h3 id="olog-n">对数阶 <span class="arithmatex">\(O(\log n)\)</span><a class="headerlink" href="#olog-n" title="Permanent link">&para;</a></h3>
<p>对数阶常见于分治算法、数据类型转换等。</p>
<p>例如「归并排序」，长度为 <span class="arithmatex">\(n\)</span> 的数组可以形成高度为 <span class="arithmatex">\(\log n\)</span> 的递归树，因此空间复杂度为 <span class="arithmatex">\(O(\log n)\)</span> 。</p>