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<h2 id="833">8.3.3. &nbsp; 方法三：堆<a class="headerlink" href="#833" title="Permanent link">&para;</a></h2>
<p>我们可以基于堆更加高效地解决 Top-K 问题，流程如下：</p>
<ol>
<li>初始化一个小顶堆，其堆顶元素最小；</li>
<li>先将数组的前 <span class="arithmatex">\(k\)</span> 个元素依次入堆；</li>
<li>从第 <span class="arithmatex">\(k + 1\)</span> 个元素开始，若当前元素大于堆顶元素，则将堆顶元素出堆，并将当前元素入堆；</li>
<li>遍历完成后，堆中保存的就是最大的 <span class="arithmatex">\(k\)</span> 个元素；</li>
<li>初始化一个小顶堆，其堆顶元素最小。</li>
<li>先将数组的前 <span class="arithmatex">\(k\)</span> 个元素依次入堆。</li>
<li>从第 <span class="arithmatex">\(k + 1\)</span> 个元素开始，若当前元素大于堆顶元素，则将堆顶元素出堆，并将当前元素入堆。</li>
<li>遍历完成后，堆中保存的就是最大的 <span class="arithmatex">\(k\)</span> 个元素。</li>
</ol>
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