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chapter_dynamic_programming/dp_problem_features/index.html
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@ -2201,7 +2201,7 @@
dp[i] = \min(dp[i-1], dp[i-2]) + cost[i]
\]</div>
<p>这便可以引出「最优子结构」的含义：<strong>原问题的最优解是从子问题的最优解构建得来的</strong>。对于本题，我们从两个子问题最优解 <span class="arithmatex">\(dp[i-1]\)</span> , <span class="arithmatex">\(dp[i-2]\)</span> 中挑选出较优的那一个，并用它构建出原问题 <span class="arithmatex">\(dp[i]\)</span> 的最优解。</p>
<p>相较于分治算法问题，动态规划问题的解也是由其子问题的解构成的。不同的是，<strong>动态规划中子问题的解不仅揭示了问题的局部最优解，而且还通过特定的递推关系链接起来，共同构建出原问题的全局最优解</strong>。</p>
<p>相较于分治问题，动态规划问题的解也是由其子问题的解构成的。不同的是，<strong>动态规划中子问题的解不仅揭示了问题的局部最优解，而且还通过特定的递推关系链接起来，共同构建出原问题的全局最优解</strong>。</p>
<p>那么，上节的爬楼梯题目有没有最优子结构呢？它要求解的是方案数量，看似是一个计数问题，但如果换一种问法：求解最大方案数量。我们意外地发现，<strong>虽然题目修改前后是等价的，但最优子结构浮现出来了</strong>：第 <span class="arithmatex">\(n\)</span> 阶最大方案数量等于第 <span class="arithmatex">\(n-1\)</span> 阶和第 <span class="arithmatex">\(n-2\)</span> 阶最大方案数量之和。所以说，最优子结构的是一个比较宽泛的概念，在不同问题中会有不同的含义。</p>
<p>根据以上状态转移方程，以及初始状态 <span class="arithmatex">\(dp[1] = cost[1]\)</span> , <span class="arithmatex">\(dp[2] = cost[2]\)</span> ，我们可以得出动态规划解题代码。</p>
<div class="tabbed-set tabbed-alternate" data-tabs="1:11"><input checked="checked" id="__tabbed_1_1" name="__tabbed_1" type="radio" /><input id="__tabbed_1_2" name="__tabbed_1" type="radio" /><input id="__tabbed_1_3" name="__tabbed_1" type="radio" /><input id="__tabbed_1_4" name="__tabbed_1" type="radio" /><input id="__tabbed_1_5" name="__tabbed_1" type="radio" /><input id="__tabbed_1_6" name="__tabbed_1" type="radio" /><input id="__tabbed_1_7" name="__tabbed_1" type="radio" /><input id="__tabbed_1_8" name="__tabbed_1" type="radio" /><input id="__tabbed_1_9" name="__tabbed_1" type="radio" /><input id="__tabbed_1_10" name="__tabbed_1" type="radio" /><input id="__tabbed_1_11" name="__tabbed_1" type="radio" /><div class="tabbed-labels"><label for="__tabbed_1_1">Java</label><label for="__tabbed_1_2">C++</label><label for="__tabbed_1_3">Python</label><label for="__tabbed_1_4">Go</label><label for="__tabbed_1_5">JavaScript</label><label for="__tabbed_1_6">TypeScript</label><label for="__tabbed_1_7">C</label><label for="__tabbed_1_8">C#</label><label for="__tabbed_1_9">Swift</label><label for="__tabbed_1_10">Zig</label><label for="__tabbed_1_11">Dart</label></div>

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chapter_dynamic_programming/intro_to_dynamic_programming/index.html
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<h1 id="131">13.1. &nbsp; 初探动态规划<a class="headerlink" href="#131" title="Permanent link">&para;</a></h1>
<p>「动态规划 Dynamic Programming」是一种用于解决复杂问题的优化算法，它把一个问题分解为一系列更小的子问题，并把子问题的解存储起来以供后续使用，从而避免了重复计算，提升了解题效率。</p>
<p>在本节中，我们先从一个动态规划经典例题入手，了解动态规划是如何高效地求解问题的。</p>
<p>在本节中，我们先从一个动态规划的经典例题入手，先给出它的暴力回溯解法，观察其中包含的重叠子问题，再一步步导出更高效的动态规划解法。</p>
<div class="admonition question">
<p class="admonition-title">爬楼梯</p>
<p>给定一个共有 <span class="arithmatex">\(n\)</span> 阶的楼梯，你每步可以上 <span class="arithmatex">\(1\)</span> 阶或者 <span class="arithmatex">\(2\)</span> 阶，请问有多少种方案可以爬到楼顶。</p>
@ -2774,6 +2774,12 @@ dp[i] = dp[i-1] + dp[i-2]
</div>
</div>
<p><strong>我们将这种空间优化技巧称为「状态压缩」</strong>。在许多动态规划问题中，当前状态仅与前面有限个状态有关，不必保存所有的历史状态，这时我们可以应用状态压缩，只保留必要的状态，通过“降维”来节省内存空间。</p>
<p>总的看来，子问题分解是一种通用的算法思路，在分治算法、动态规划、回溯算法中各有特点：</p>
<ul>
<li>分治算法将原问题划分为几个独立的子问题，然后递归解决子问题，最后合并子问题的解得到原问题的解。例如，归并排序将长数组不断划分为两个短子数组，再将排序好的子数组合并为排序好的长数组。</li>
<li>动态规划也是将原问题分解为多个子问题，但与分治算法的主要区别是，<strong>动态规划中的子问题往往不是相互独立的</strong>，原问题的解依赖于子问题的解，而子问题的解又依赖于更小的子问题的解。因此，动态规划通常会引入记忆化，保存已经解决的子问题的解，避免重复计算。</li>
<li>回溯算法在尝试和回退中穷举所有可能的解，并通过剪枝避免不必要的搜索分支。原问题的解由一系列决策步骤构成，我们可以将每个决策步骤之后的剩余问题看作为一个子问题。</li>
</ul>

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sitemap.xml
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