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chapter_computational_complexity/summary/index.html

@@ -710,8 +710,15 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#251-q-a" class="md-nav__link">
-    2.5.1 &nbsp; Q &amp; A
+  <a href="#1" class="md-nav__link">
+    1. &nbsp; 重点回顾
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#2-q-a" class="md-nav__link">
+    2. &nbsp; Q &amp; A
   </a>
   
 </li>
@@ -3398,8 +3405,15 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#251-q-a" class="md-nav__link">
-    2.5.1 &nbsp; Q &amp; A
+  <a href="#1" class="md-nav__link">
+    1. &nbsp; 重点回顾
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#2-q-a" class="md-nav__link">
+    2. &nbsp; Q &amp; A
   </a>
   
 </li>
@@ -3428,6 +3442,7 @@
 
 
 <h1 id="25">2.5 &nbsp; 小结<a class="headerlink" href="#25" title="Permanent link">&para;</a></h1>
+<h3 id="1">1. &nbsp; 重点回顾<a class="headerlink" href="#1" title="Permanent link">&para;</a></h3>
 <p><strong>算法效率评估</strong></p>
 <ul>
 <li>时间效率和空间效率是衡量算法优劣的两个主要评价指标。</li>
@@ -3450,7 +3465,7 @@
 <li>我们通常只关注最差空间复杂度，即统计算法在最差输入数据和最差运行时间点下的空间复杂度。</li>
 <li>常见空间复杂度从小到大排列有 <span class="arithmatex">\(O(1)\)</span>、<span class="arithmatex">\(O(\log n)\)</span>、<span class="arithmatex">\(O(n)\)</span>、<span class="arithmatex">\(O(n^2)\)</span> 和 <span class="arithmatex">\(O(2^n)\)</span> 等。</li>
 </ul>
-<h2 id="251-q-a">2.5.1 &nbsp; Q &amp; A<a class="headerlink" href="#251-q-a" title="Permanent link">&para;</a></h2>
+<h3 id="2-q-a">2. &nbsp; Q &amp; A<a class="headerlink" href="#2-q-a" title="Permanent link">&para;</a></h3>
 <div class="admonition question">
 <p class="admonition-title">尾递归的空间复杂度是 <span class="arithmatex">\(O(1)\)</span> 吗？</p>
 <p>理论上，尾递归函数的空间复杂度可以被优化至 <span class="arithmatex">\(O(1)\)</span> 。不过绝大多数编程语言（例如 Java、Python、C++、Go、C# 等）都不支持自动优化尾递归，因此通常认为空间复杂度是 <span class="arithmatex">\(O(n)\)</span> 。</p>