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<p class="admonition-title">Tip</p>
<p>类似于排序算法中的“从小到大排列”和“从大到小排列”，我们可以通过修改 Comparator 来实现“小顶堆”与“大顶堆”之间的转换。</p>
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<div class="highlight"><span class="filename">heap.java</span><pre><span></span><code><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="cm">/* 初始化堆 */</span>
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<p align="center"> Fig. 堆的表示与存储 </p>
<p>我们可以将索引映射公式封装成函数，方便后续使用。</p>
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<div class="highlight"><span class="filename">my_heap.java</span><pre><span></span><code><a id="__codelineno-12-1" name="__codelineno-12-1" href="#__codelineno-12-1"></a><span class="cm">/* 获取左子节点索引 */</span>
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<h3 id="_2">访问堆顶元素<a class="headerlink" href="#_2" title="Permanent link">&para;</a></h3>
<p>堆顶元素即为二叉树的根节点，也就是列表的首个元素。</p>
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<div class="highlight"><span class="filename">my_heap.java</span><pre><span></span><code><a id="__codelineno-24-1" name="__codelineno-24-1" href="#__codelineno-24-1"></a><span class="cm">/* 访问堆顶元素 */</span>
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<p>设节点总数为 <span class="arithmatex">\(n\)</span> ，则树的高度为 <span class="arithmatex">\(O(\log n)\)</span> 。由此可知，堆化操作的循环轮数最多为 <span class="arithmatex">\(O(\log n)\)</span> ，<strong>元素入堆操作的时间复杂度为 <span class="arithmatex">\(O(\log n)\)</span></strong> 。</p>
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<div class="highlight"><span class="filename">my_heap.java</span><pre><span></span><code><a id="__codelineno-36-1" name="__codelineno-36-1" href="#__codelineno-36-1"></a><span class="cm">/* 元素入堆 */</span>
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<p>与元素入堆操作相似，堆顶元素出堆操作的时间复杂度也为 <span class="arithmatex">\(O(\log n)\)</span> 。</p>
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<div class="highlight"><span class="filename">my_heap.java</span><pre><span></span><code><a id="__codelineno-48-1" name="__codelineno-48-1" href="#__codelineno-48-1"></a><span class="cm">/* 元素出堆 */</span>