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chapter_computational_complexity/space_complexity/index.html
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</div>
<h2 id="232">2.3.2 &nbsp; 推算方法<a class="headerlink" href="#232" title="Permanent link">&para;</a></h2>
<p>空间复杂度的推算方法与时间复杂度大致相同，只需将统计对象从“操作数量”转为“使用空间大小”。</p>
<p>而与时间复杂度不同的是，<strong>我们通常只关注「最差空间复杂度」</strong>。这是因为内存空间是一项硬性要求，我们必须确保在所有输入数据下都有足够的内存空间预留。</p>
<p>而与时间复杂度不同的是，<strong>我们通常只关注最差空间复杂度</strong>。这是因为内存空间是一项硬性要求，我们必须确保在所有输入数据下都有足够的内存空间预留。</p>
<p>观察以下代码，最差空间复杂度中的“最差”有两层含义。</p>
<ol>
<li><strong>以最差输入数据为准</strong>：当 <span class="arithmatex">\(n &lt; 10\)</span> 时，空间复杂度为 <span class="arithmatex">\(O(1)\)</span> ；但当 <span class="arithmatex">\(n &gt; 10\)</span> 时，初始化的数组 <code>nums</code> 占用 <span class="arithmatex">\(O(n)\)</span> 空间；因此最差空间复杂度为 <span class="arithmatex">\(O(n)\)</span> 。</li>
@ -5134,7 +5134,7 @@ O(1) &lt; O(\log n) &lt; O(n) &lt; O(n^2) &lt; O(2^n) \newline
<p align="center"> 图：递归函数产生的平方阶空间复杂度 </p>
<h3 id="4-o2n">4. &nbsp; 指数阶 <span class="arithmatex">\(O(2^n)\)</span><a class="headerlink" href="#4-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」的节点数量为 <span class="arithmatex">\(2^n - 1\)</span> ，占用 <span class="arithmatex">\(O(2^n)\)</span> 空间：</p>
<p>指数阶常见于二叉树。高度为 <span class="arithmatex">\(n\)</span> 的“满二叉树”的节点数量为 <span class="arithmatex">\(2^n - 1\)</span> ，占用 <span class="arithmatex">\(O(2^n)\)</span> 空间：</p>
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