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dp[i] = \min(dp[i-1], dp[i-2]) + cost[i]
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dp[i] = \min(dp[i-1], dp[i-2]) + cost[i]
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\]</div>
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\]</div>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<h1 id="131">13.1. 初探动态规划<a class="headerlink" href="#131" title="Permanent link">¶</a></h1>
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<h1 id="131">13.1. 初探动态规划<a class="headerlink" href="#131" title="Permanent link">¶</a></h1>
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<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>
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<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>
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<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>
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<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>
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<div class="admonition question">
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<div class="admonition question">
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<p class="admonition-title">爬楼梯</p>
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<p class="admonition-title">爬楼梯</p>
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<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>
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<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>
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</div>
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</div>
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</div>
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</div>
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<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>
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<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>
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<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>
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<ul>
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<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>
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<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>
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<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>
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<lastmod>2023-07-01</lastmod>
|
<lastmod>2023-07-03</lastmod>
|
||||||
<changefreq>daily</changefreq>
|
<changefreq>daily</changefreq>
|
||||||
</url>
|
</url>
|
||||||
<url>
|
<url>
|
||||||
<loc>https://www.hello-algo.com/chapter_sorting/quick_sort/</loc>
|
<loc>https://www.hello-algo.com/chapter_sorting/quick_sort/</loc>
|
||||||
<lastmod>2023-07-01</lastmod>
|
<lastmod>2023-07-03</lastmod>
|
||||||
<changefreq>daily</changefreq>
|
<changefreq>daily</changefreq>
|
||||||
</url>
|
</url>
|
||||||
<url>
|
<url>
|
||||||
<loc>https://www.hello-algo.com/chapter_sorting/radix_sort/</loc>
|
<loc>https://www.hello-algo.com/chapter_sorting/radix_sort/</loc>
|
||||||
<lastmod>2023-07-01</lastmod>
|
<lastmod>2023-07-03</lastmod>
|
||||||
<changefreq>daily</changefreq>
|
<changefreq>daily</changefreq>
|
||||||
</url>
|
</url>
|
||||||
<url>
|
<url>
|
||||||
<loc>https://www.hello-algo.com/chapter_sorting/selection_sort/</loc>
|
<loc>https://www.hello-algo.com/chapter_sorting/selection_sort/</loc>
|
||||||
<lastmod>2023-07-01</lastmod>
|
<lastmod>2023-07-03</lastmod>
|
||||||
<changefreq>daily</changefreq>
|
<changefreq>daily</changefreq>
|
||||||
</url>
|
</url>
|
||||||
<url>
|
<url>
|
||||||
<loc>https://www.hello-algo.com/chapter_sorting/sorting_algorithm/</loc>
|
<loc>https://www.hello-algo.com/chapter_sorting/sorting_algorithm/</loc>
|
||||||
<lastmod>2023-07-01</lastmod>
|
<lastmod>2023-07-03</lastmod>
|
||||||
<changefreq>daily</changefreq>
|
<changefreq>daily</changefreq>
|
||||||
</url>
|
</url>
|
||||||
<url>
|
<url>
|
||||||
<loc>https://www.hello-algo.com/chapter_sorting/summary/</loc>
|
<loc>https://www.hello-algo.com/chapter_sorting/summary/</loc>
|
||||||
<lastmod>2023-07-01</lastmod>
|
<lastmod>2023-07-03</lastmod>
|
||||||
<changefreq>daily</changefreq>
|
<changefreq>daily</changefreq>
|
||||||
</url>
|
</url>
|
||||||
<url>
|
<url>
|
||||||
<loc>https://www.hello-algo.com/chapter_stack_and_queue/</loc>
|
<loc>https://www.hello-algo.com/chapter_stack_and_queue/</loc>
|
||||||
<lastmod>2023-07-01</lastmod>
|
<lastmod>2023-07-03</lastmod>
|
||||||
<changefreq>daily</changefreq>
|
<changefreq>daily</changefreq>
|
||||||
</url>
|
</url>
|
||||||
<url>
|
<url>
|
||||||
<loc>https://www.hello-algo.com/chapter_stack_and_queue/deque/</loc>
|
<loc>https://www.hello-algo.com/chapter_stack_and_queue/deque/</loc>
|
||||||
<lastmod>2023-07-01</lastmod>
|
<lastmod>2023-07-03</lastmod>
|
||||||
<changefreq>daily</changefreq>
|
<changefreq>daily</changefreq>
|
||||||
</url>
|
</url>
|
||||||
<url>
|
<url>
|
||||||
<loc>https://www.hello-algo.com/chapter_stack_and_queue/queue/</loc>
|
<loc>https://www.hello-algo.com/chapter_stack_and_queue/queue/</loc>
|
||||||
<lastmod>2023-07-01</lastmod>
|
<lastmod>2023-07-03</lastmod>
|
||||||
<changefreq>daily</changefreq>
|
<changefreq>daily</changefreq>
|
||||||
</url>
|
</url>
|
||||||
<url>
|
<url>
|
||||||
<loc>https://www.hello-algo.com/chapter_stack_and_queue/stack/</loc>
|
<loc>https://www.hello-algo.com/chapter_stack_and_queue/stack/</loc>
|
||||||
<lastmod>2023-07-01</lastmod>
|
<lastmod>2023-07-03</lastmod>
|
||||||
<changefreq>daily</changefreq>
|
<changefreq>daily</changefreq>
|
||||||
</url>
|
</url>
|
||||||
<url>
|
<url>
|
||||||
<loc>https://www.hello-algo.com/chapter_stack_and_queue/summary/</loc>
|
<loc>https://www.hello-algo.com/chapter_stack_and_queue/summary/</loc>
|
||||||
<lastmod>2023-07-01</lastmod>
|
<lastmod>2023-07-03</lastmod>
|
||||||
<changefreq>daily</changefreq>
|
<changefreq>daily</changefreq>
|
||||||
</url>
|
</url>
|
||||||
<url>
|
<url>
|
||||||
<loc>https://www.hello-algo.com/chapter_tree/</loc>
|
<loc>https://www.hello-algo.com/chapter_tree/</loc>
|
||||||
<lastmod>2023-07-01</lastmod>
|
<lastmod>2023-07-03</lastmod>
|
||||||
<changefreq>daily</changefreq>
|
<changefreq>daily</changefreq>
|
||||||
</url>
|
</url>
|
||||||
<url>
|
<url>
|
||||||
<loc>https://www.hello-algo.com/chapter_tree/array_representation_of_tree/</loc>
|
<loc>https://www.hello-algo.com/chapter_tree/array_representation_of_tree/</loc>
|
||||||
<lastmod>2023-07-01</lastmod>
|
<lastmod>2023-07-03</lastmod>
|
||||||
<changefreq>daily</changefreq>
|
<changefreq>daily</changefreq>
|
||||||
</url>
|
</url>
|
||||||
<url>
|
<url>
|
||||||
<loc>https://www.hello-algo.com/chapter_tree/avl_tree/</loc>
|
<loc>https://www.hello-algo.com/chapter_tree/avl_tree/</loc>
|
||||||
<lastmod>2023-07-01</lastmod>
|
<lastmod>2023-07-03</lastmod>
|
||||||
<changefreq>daily</changefreq>
|
<changefreq>daily</changefreq>
|
||||||
</url>
|
</url>
|
||||||
<url>
|
<url>
|
||||||
<loc>https://www.hello-algo.com/chapter_tree/binary_search_tree/</loc>
|
<loc>https://www.hello-algo.com/chapter_tree/binary_search_tree/</loc>
|
||||||
<lastmod>2023-07-01</lastmod>
|
<lastmod>2023-07-03</lastmod>
|
||||||
<changefreq>daily</changefreq>
|
<changefreq>daily</changefreq>
|
||||||
</url>
|
</url>
|
||||||
<url>
|
<url>
|
||||||
<loc>https://www.hello-algo.com/chapter_tree/binary_tree/</loc>
|
<loc>https://www.hello-algo.com/chapter_tree/binary_tree/</loc>
|
||||||
<lastmod>2023-07-01</lastmod>
|
<lastmod>2023-07-03</lastmod>
|
||||||
<changefreq>daily</changefreq>
|
<changefreq>daily</changefreq>
|
||||||
</url>
|
</url>
|
||||||
<url>
|
<url>
|
||||||
<loc>https://www.hello-algo.com/chapter_tree/binary_tree_traversal/</loc>
|
<loc>https://www.hello-algo.com/chapter_tree/binary_tree_traversal/</loc>
|
||||||
<lastmod>2023-07-01</lastmod>
|
<lastmod>2023-07-03</lastmod>
|
||||||
<changefreq>daily</changefreq>
|
<changefreq>daily</changefreq>
|
||||||
</url>
|
</url>
|
||||||
<url>
|
<url>
|
||||||
<loc>https://www.hello-algo.com/chapter_tree/summary/</loc>
|
<loc>https://www.hello-algo.com/chapter_tree/summary/</loc>
|
||||||
<lastmod>2023-07-01</lastmod>
|
<lastmod>2023-07-03</lastmod>
|
||||||
<changefreq>daily</changefreq>
|
<changefreq>daily</changefreq>
|
||||||
</url>
|
</url>
|
||||||
</urlset>
|
</urlset>
|
BIN
sitemap.xml.gz
BIN
sitemap.xml.gz
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Loading…
Reference in a new issue