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<h1 id="116">11.6. &nbsp; 小结<a class="headerlink" href="#116" 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。通过增加标志位实现提前返回,我们可将冒泡排序的最佳时间复杂度优化至 <span class="arithmatex">\(O(N)\)</span></li>
<li>插入排序每轮将待排序区间内元素插入至已排序区间的正确位置,从而实现排序。插入排序的时间复杂度虽为 <span class="arithmatex">\(O(N^2)\)</span> ,但因为总体操作少而很受欢迎,一般用于小数据量的排序工作。</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,从而导致时间复杂度劣化至 <span class="arithmatex">\(O(N^2)\)</span> ,通过引入中位数基准数或随机基准数可大大降低劣化概率。尾递归方法可以有效减小递归深度,将空间复杂度优化至 <span class="arithmatex">\(O(\log N)\)</span></li>
<li>归并排序包含划分和合并两个阶段,是分而治之的标准体现。对于归并排序,排序数组需要借助辅助数组,空间复杂度为 <span class="arithmatex">\(O(N)\)</span> ;而排序链表的空间复杂度可以被优化至 <span class="arithmatex">\(O(1)\)</span></li>
<li>下图总结对比了各个排序算法的运行效率与特性。其中,桶排序中 <span class="arithmatex">\(k\)</span> 为桶的数量;基数排序仅适用于正整数、字符串、特定格式的浮点数,<span class="arithmatex">\(k\)</span> 为最大数字的位数。</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,如同其它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>
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