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<h1 id="119">11.9. &nbsp; 小结<a class="headerlink" href="#119" title="Permanent link">&para;</a></h1>
<ul>
<li>冒泡排序通过交换相邻元素来实现排序。通过添加一个标志位来实现提前返回,我们可以将冒泡排序的最佳时间复杂度优化到 <span class="arithmatex">\(O(n)\)</span></li>
<li>插入排序每轮将待排序区间内的元素插入到已排序区间的正确位置,从而完成排序。虽然插入排序的时间复杂度为 <span class="arithmatex">\(O(n^2)\)</span> ,但由于单元操作相对较少,它在小数据量的排序任务中非常受欢迎。</li>
<li>快速排序基于哨兵划分操作实现排序。在哨兵划分中,有可能每次都选取到最差的基准数,导致时间复杂度劣化至 <span class="arithmatex">\(O(n^2)\)</span> 。引入中位数基准数或随机基准数可以降低这种劣化的概率。尾递归方法可以有效地减少递归深度,将空间复杂度优化到 <span class="arithmatex">\(O(\log n)\)</span></li>
<li>归并排序包括划分和合并两个阶段,典型地体现了分治策略。在归并排序中,排序数组需要创建辅助数组,空间复杂度为 <span class="arithmatex">\(O(n)\)</span> ;然而排序链表的空间复杂度可以优化至 <span class="arithmatex">\(O(1)\)</span></li>
<li>桶排序包含三个步骤:数据分桶、桶内排序和合并结果。它同样体现了分治策略,适用于数据体量很大的情况。桶排序的关键在于对数据进行平均分配。</li>
<li>计数排序是桶排序的一个特例,它通过统计数据出现的次数来实现排序。计数排序适用于数据量大但数据范围有限的情况,并且要求数据能够转换为正整数。</li>
<li>基数排序通过逐位排序来实现数据排序,要求数据能够表示为固定位数的数字。</li>
</ul>
<p><img alt="排序算法对比" src="../summary.assets/sorting_algorithms_comparison.png" /></p>
<p align="center"> Fig. 排序算法对比 </p>
<ul>
<li>总体来看,我们追求运行快、稳定、原地、正向自适应性的排序。显然,如同其他数据结构与算法一样,同时满足这些条件的排序算法并不存在,我们需要根据问题特点来选择排序算法。</li>
<li>总的来说,我们希望找到一种排序算法,具有高效率、稳定、原地以及正向自适应性等优点。然而,正如其他数据结构和算法一样,没有一种排序算法能够同时满足所有这些条件。在实际应用中,我们需要根据数据的特性来选择合适的排序算法。</li>
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