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第 6 章 &nbsp; 哈希表
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第 11 章 &nbsp; 排序
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第 12 章 &nbsp; 分治
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<h1 id="135">13.5 &nbsp; 小结<a class="headerlink" href="#135" title="Permanent link">&para;</a></h1>
<h3 id="1">1. &nbsp; 重点回顾<a class="headerlink" href="#1" title="Permanent link">&para;</a></h3>
<ul>
<li>回溯算法本质是穷举法,通过对解空间进行深度优先遍历来寻找符合条件的解。在搜索过程中,遇到满足条件的解则记录,直至找到所有解或遍历完成后结束。</li>
<li>回溯算法的搜索过程包括尝试与回退两个部分。它通过深度优先搜索来尝试各种选择,当遇到不满足约束条件的情况时,则撤销上一步的选择,退回到之前的状态,并继续尝试其他选择。尝试与回退是两个方向相反的操作。</li>
<li>回溯问题通常包含多个约束条件,它们可用于实现剪枝操作。剪枝可以提前结束不必要的搜索分支,大幅提升搜索效率。</li>
<li>回溯算法主要可用于解决搜索问题和约束满足问题。组合优化问题虽然可以用回溯算法解决,但往往存在效率更高或效果更好的解法。</li>
<li>全排列问题旨在搜索给定集合元素的所有可能的排列。我们借助一个数组来记录每个元素是否被选择,剪掉重复选择同一元素的搜索分支,确保每个元素只被选择一次。</li>
<li>在全排列问题中,如果集合中存在重复元素,则最终结果会出现重复排列。我们需要约束相等元素在每轮中只能被选择一次,这通常借助一个哈希表来实现。</li>
<li>子集和问题的目标是在给定集合中找到和为目标值的所有子集。集合不区分元素顺序,而搜索过程会输出所有顺序的结果,产生重复子集。我们在回溯前将数据进行排序,并设置一个变量来指示每一轮的遍历起始点,从而将生成重复子集的搜索分支进行剪枝。</li>
<li>对于子集和问题,数组中的相等元素会产生重复集合。我们利用数组已排序的前置条件,通过判断相邻元素是否相等实现剪枝,从而确保相等元素在每轮中只能被选中一次。</li>
<li><span class="arithmatex">\(n\)</span> 皇后问题旨在寻找将 <span class="arithmatex">\(n\)</span> 个皇后放置到 <span class="arithmatex">\(n \times n\)</span> 尺寸棋盘上的方案,要求所有皇后两两之间无法攻击对方。该问题的约束条件有行约束、列约束、主对角线和副对角线约束。为满足行约束,我们采用按行放置的策略,保证每一行放置一个皇后。</li>
<li>列约束和对角线约束的处理方式类似。对于列约束,我们利用一个数组来记录每一列是否有皇后,从而指示选中的格子是否合法。对于对角线约束,我们借助两个数组来分别记录该主、副对角线上是否存在皇后;难点在于找处在到同一主(副)对角线上格子满足的行列索引规律。</li>
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<h3 id="2-q-a">2. &nbsp; Q &amp; A<a class="headerlink" href="#2-q-a" title="Permanent link">&para;</a></h3>
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<p class="admonition-title">怎么理解回溯和递归的关系?</p>
<p>总的来看,回溯是一种“算法策略”,而递归更像是一个“工具”。</p>
<ul>
<li>回溯算法通常基于递归实现。然而,回溯是递归的应用场景之一,是递归在搜索问题中的应用。</li>
<li>递归的结构体现了“子问题分解”的解题范式,常用于解决分治、回溯、动态规划(记忆化递归)等问题。</li>
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