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5.3. &nbsp; 双向队列
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10.2. &nbsp; 二分查找边界
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11.2. &nbsp; 选择排序
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11.6. &nbsp; 归并排序
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11.8. &nbsp; 桶排序
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11.9. &nbsp; 计数排序
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11.10. &nbsp; 基数排序
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<h1 id="121">12.1. &nbsp; 分治算法<a class="headerlink" href="#121" title="Permanent link">&para;</a></h1>
<p>「分治 Divide and Conquer」全称分而治之是一种非常重要的算法策略。分治通常基于递归实现包括“分”和“治”两部分主要步骤如下</p>
<ol>
<li><strong>分(划分阶段)</strong>:递归地将原问题分解为两个或多个子问题,直至到达最小子问题时终止;</li>
<li><strong>治(合并阶段)</strong>:从已知解的最小子问题开始,从底至顶地将子问题的解进行合并,从而构建出原问题的解;</li>
</ol>
<p>之前学过的「归并排序」是分治策略的典型应用之一,对于该算法:</p>
<ol>
<li><strong></strong>:递归地将原数组(原问题)划分为两个子数组(子问题),直到子数组只剩一个元素(最小子问题)。</li>
<li><strong></strong>:从底至顶地将有序的子数组进行合并,从而得到有序的原数组。</li>
</ol>
<p><img alt="归并排序的分治策略" src="../divide_and_conquer.assets/divide_and_conquer_merge_sort.png" /></p>
<p align="center"> Fig. 归并排序的分治策略 </p>
<h2 id="1211">12.1.1. &nbsp; 如何判断分治问题<a class="headerlink" href="#1211" title="Permanent link">&para;</a></h2>
<p>一个问题是否适合使用分治解决,通常可以参考以下几个判断依据:</p>
<ol>
<li><strong>问题可以被分解</strong>:原问题可以被分解成规模更小、类似的子问题,以及能够以相同方式递归地进行划分。</li>
<li><strong>子问题是独立的</strong>:子问题之间是没有重叠的,互相没有依赖,可以被独立解决。</li>
<li><strong>子问题的解可以被合并</strong>:原问题的解可以通过合并子问题的解得来。</li>
</ol>
<p>归并排序显然是满足以上三条判断依据的:</p>
<ol>
<li>递归地将数组(原问题)划分为两个子数组(子问题),当子数组只有一个元素时天然有序;</li>
<li>每个子数组都可以独立地进行排序,因此子问题是独立的;</li>
<li>两个有序子数组(子问题的解)可以被合并为一个有序数组(原问题的解);</li>
</ol>
<h2 id="1212">12.1.2. &nbsp; 通过分治提升效率<a class="headerlink" href="#1212" title="Permanent link">&para;</a></h2>
<p>分治不仅可以有效地解决算法问题,<strong>往往还可以提升算法效率</strong>。在排序算法中,归并排序相较于选择、冒泡、插入排序更快,就是因为其应用了分治策略。</p>
<p>那么,我们不禁发问:<strong>为什么分治可以提升算法效率,其底层逻辑是什么</strong>?换句话说,将大问题分解为多个子问题、解决子问题、将子问题的解合并为原问题的解,这三步为什么比直接解决原问题效率更高?这个问题可以从操作数量和并行计算两方面来讨论。</p>
<h3 id="_1">操作数量优化<a class="headerlink" href="#_1" title="Permanent link">&para;</a></h3>
<p>以「冒泡排序」为例,其处理一个长度为 <span class="arithmatex">\(n\)</span> 的数组需要 <span class="arithmatex">\(O(n^2)\)</span> 时间。假设我们把数组从中点分为两个子数组,则划分需要 <span class="arithmatex">\(O(n)\)</span> 时间,排序每个子数组需要 <span class="arithmatex">\(O((n/2)^2)\)</span> 时间,合并两个子数组需要 <span class="arithmatex">\(O(n)\)</span> 时间,总体时间复杂度为:</p>
<div class="arithmatex">\[
O(n + (n/2)^2 \times 2 + n) = O(n^2 / 2 + 2n)
\]</div>
<p><img alt="划分数组前后的冒泡排序" src="../divide_and_conquer.assets/divide_and_conquer_bubble_sort.png" /></p>
<p align="center"> Fig. 划分数组前后的冒泡排序 </p>
<p>接下来,我们计算以下不等式,其左边和右边分别为划分前和划分后的操作总数:</p>
<div class="arithmatex">\[
\begin{aligned}
n^2 &amp; &gt; \frac{n^2}{2} + 2n \newline
n^2 - \frac{n^2}{2} - 2n &amp; &gt; 0 \newline
n(n - 4) &amp; &gt; 0
\end{aligned}
\]</div>
<p><strong>这意味着当 <span class="arithmatex">\(n &gt; 4\)</span> 时,划分后的操作数量更少,排序效率可能更高</strong>。当然,划分后的时间复杂度仍然是平方阶 <span class="arithmatex">\(O(n^2)\)</span> ,即复杂度并没有降低,只是其中的常数项变小了。</p>
<p>那么,<strong>如果我们把子数组不断地再从中点划分为两个子数组</strong>,直至子数组只剩一个元素时停止划分呢?这就达到了「归并排序」的情况,时间复杂度为 <span class="arithmatex">\(O(n \log n)\)</span></p>
<p>再思考,<strong>如果我们多设置几个划分点</strong>,将原数组平均划分为 <span class="arithmatex">\(k\)</span> 个子数组呢?这种情况就与「桶排序」非常类似了,它非常适合排序海量数据,理论上时间复杂度可以达到 <span class="arithmatex">\(O(n + k)\)</span></p>
<h3 id="_2">并行计算优化<a class="headerlink" href="#_2" title="Permanent link">&para;</a></h3>
<p>我们知道,分治生成的子问题是相互独立的,<strong>因此通常可以并行解决</strong>。也就是说,分治不仅可以降低算法的时间复杂度,<strong>还有利于操作系统的并行优化</strong></p>
<p>并行优化在多核或多处理器的环境中尤其有效,因为系统可以同时处理多个子问题,更加充分地利用计算资源,从而显著减少总体的运行时间。</p>
<p>例如在桶排序中,我们将海量的数据平均分配到各个桶中,则可所有桶的排序任务分散到各个计算单元,完成后再进行结果合并。</p>
<p><img alt="桶排序的并行计算" src="../divide_and_conquer.assets/divide_and_conquer_parallel_computing.png" /></p>
<p align="center"> Fig. 桶排序的并行计算 </p>
<h2 id="1213">12.1.3. &nbsp; 分治常见应用<a class="headerlink" href="#1213" title="Permanent link">&para;</a></h2>
<p>一方面,分治可以用来解决许多经典算法问题:</p>
<ul>
<li><strong>寻找最近点对</strong>:该算法首先将点集分成两部分,然后分别找出两部分中的最近点对,最后再找出跨越两部分的最近点对。</li>
<li><strong>大整数乘法</strong>:例如 Karatsuba 算法,它是将大整数乘法分解为几个较小的整数的乘法和加法。</li>
<li><strong>矩阵乘法</strong>:例如 Strassen 算法,它是将大矩阵乘法分解为多个小矩阵的乘法和加法。</li>
<li><strong>汉诺塔问题</strong>:汉诺塔问题可以视为典型的分治策略,通过递归解决。</li>
<li><strong>求解逆序对</strong>:在一个序列中,如果前面的数字大于后面的数字,那么这两个数字构成一个逆序对。求解逆序对问题可以通过分治的思想,借助归并排序进行求解。</li>
</ul>
<p>另一方面,<strong>分治在算法和数据结构的设计中应用非常广泛</strong>,举几个已经学过的例子:</p>
<ul>
<li><strong>二分查找</strong>:二分查找是将有序数组从中点索引分为两部分,然后根据目标值与中间元素值比较结果,决定排除哪一半区间,然后在剩余区间执行相同的二分操作。</li>
<li><strong>归并排序</strong>:文章开头已介绍,不再赘述。</li>
<li><strong>快速排序</strong>:快速排序是选取一个基准值,然后把数组分为两个子数组,一个子数组的元素比基准值小,另一子数组的元素比基准值大,然后再对这两部分进行相同的划分操作,直至子数组只剩下一个元素。</li>
<li><strong>桶排序</strong>:桶排序的基本思想是将数据分散到多个桶,然后对每个桶内的元素进行排序,最后将各个桶的元素依次取出,从而得到一个有序数组。</li>
<li><strong></strong>例如二叉搜索树、AVL 树、红黑树、B 树、B+ 树等,它们的查找、插入和删除等操作都可以视为分治的应用。</li>
<li><strong></strong>:堆是一种特殊的完全二叉树,其各种操作,如插入、删除和堆化,实际上都隐含了分治的思想。</li>
<li><strong>哈希表</strong>:虽然哈希表来并不直接应用分治,但某些哈希冲突解决策略间接应用了分治策略,例如,链式地址中的长链表会被转化为红黑树,以提升查询效率。</li>
</ul>
<p>可以看出,<strong>分治是一种“润物细无声”的算法思想</strong>,隐含在各种算法与数据结构之中。</p>
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