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<h1 id="142">14.2 &nbsp; 动态规划问题特性<a class="headerlink" href="#142" title="Permanent link">&para;</a></h1>
<p>在上一节中,我们学习了动态规划是如何通过子问题分解来求解原问题的。实际上,子问题分解是一种通用的算法思路,在分治、动态规划、回溯中的侧重点不同。</p>
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<h1 id="143">14.3 &nbsp; 动态规划解题思路<a class="headerlink" href="#143" title="Permanent link">&para;</a></h1>
<p>上两节介绍了动态规划问题的主要特征,接下来我们一起探究两个更加实用的问题。</p>
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<h1 id="146">14.6 &nbsp; 编辑距离问题<a class="headerlink" href="#146" title="Permanent link">&para;</a></h1>
<p>编辑距离,也称 Levenshtein 距离,指两个字符串之间互相转换的最少修改次数,通常用于在信息检索和自然语言处理中度量两个序列的相似度。</p>
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<h1 id="14">第 14 章 &nbsp; 动态规划<a class="headerlink" href="#14" title="Permanent link">&para;</a></h1>
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<h1 id="141">14.1 &nbsp; 初探动态规划<a class="headerlink" href="#141" title="Permanent link">&para;</a></h1>
<p>「动态规划 dynamic programming」是一个重要的算法范式,它将一个问题分解为一系列更小的子问题,并通过存储子问题的解来避免重复计算,从而大幅提升时间效率。</p>
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<h1 id="144-0-1">14.4 &nbsp; 0-1 背包问题<a class="headerlink" href="#144-0-1" title="Permanent link">&para;</a></h1>
<p>背包问题是一个非常好的动态规划入门题目,是动态规划中最常见的问题形式。其具有很多变种,例如 0-1 背包问题、完全背包问题、多重背包问题等。</p>
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<h1 id="147">14.7 &nbsp; 小结<a class="headerlink" href="#147" title="Permanent link">&para;</a></h1>
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<h1 id="145">14.5 &nbsp; 完全背包问题<a class="headerlink" href="#145" title="Permanent link">&para;</a></h1>
<p>在本节中,我们先求解另一个常见的背包问题:完全背包,再了解它的一种特例:零钱兑换。</p>