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add visualization
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‎动态规划系列/LCS.md‎

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```
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<visual slug='longest-common-subsequence'/>
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<a href="https://labuladong.online/algo-visualize/leetcode/longest-common-subsequence/" target="_blank">
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<details style="max-width:90%;max-height:400px">
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<strong>🍭 代码可视化动画🍭</strong>
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<visual slug='minimum-ascii-delete-sum-for-two-strings'/>
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<a href="https://labuladong.online/algo-visualize/leetcode/minimum-ascii-delete-sum-for-two-strings/" target="_blank">
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<details style="max-width:90%;max-height:400px">
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‎动态规划系列/动态规划之正则表达.md‎

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<visual slug='regular-expression-matching'/>
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<a href="https://labuladong.online/algo-visualize/leetcode/regular-expression-matching/" target="_blank">
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‎动态规划系列/动态规划设计：最长递增子序列.md‎

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<visual slug='longest-increasing-subsequence'/>
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<a href="https://labuladong.online/algo-visualize/leetcode/longest-increasing-subsequence/" target="_blank">
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<details style="max-width:90%;max-height:400px">
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<visual slug='mydata-lis'/>
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<a href="https://labuladong.online/algo-visualize/tutorial/mydata-lis/" target="_blank">
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<details style="max-width:90%;max-height:400px">
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<strong>🍭 代码可视化动画🍭</strong>
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<visual slug='russian-doll-envelopes'/>
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<a href="https://labuladong.online/algo-visualize/leetcode/russian-doll-envelopes/" target="_blank">
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<details style="max-width:90%;max-height:400px">
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<strong>🌟 代码可视化动画🌟</strong>
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‎动态规划系列/动态规划详解进阶.md‎

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<visual slug='mydata-fib'/>
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<a href="https://labuladong.online/algo-visualize/tutorial/mydata-fib/" target="_blank">
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<visual slug='mydata-fib2' />
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<a href="https://labuladong.online/algo-visualize/tutorial/mydata-fib2/" target="_blank">
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<details style="max-width:90%;max-height:400px">
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<visual slug='mydata-fib3' />
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<a href="https://labuladong.online/algo-visualize/tutorial/mydata-fib3/" target="_blank">
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<strong>🌟 代码可视化动画🌟</strong>
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<visual slug='fibonacci-number' />
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<a href="https://labuladong.online/algo-visualize/leetcode/fibonacci-number/" target="_blank">
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> 另外，我经常看到有读者留言问，子问题的结果为什么要加 1（`subProblem + 1`），而不是加硬币金额之类的。我这里统一提示一下，动态规划问题的关键是 `dp` 函数/数组的定义，你这个函数的返回值代表什么？你回过头去搞清楚这一点，然后就知道为什么要给子问题的返回值加 1 了。
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<visual slug='coin-change-brute-force'/>
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<a href="https://labuladong.online/algo-visualize/tutorial/coin-change-brute-force/" target="_blank">
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至此，状态转移方程其实已经完成了，以上算法已经是暴力解法了，以上代码的数学形式就是状态转移方程：
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<visual slug='coin-change' />
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<a href="https://labuladong.online/algo-visualize/leetcode/coin-change/" target="_blank">
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<details style="max-width:90%;max-height:400px">
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<strong>🥳 代码可视化动画🥳</strong>
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‎动态规划系列/单词拼接.md‎

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> 注意到计算 `prefix` 的过程中，我们是直接调用编程语言提供的子串截取函数，这个函数的时间复杂度是 $O(N)$。不难发现截取子串的开始索引是固定的 `i`，结束索引是递增的 `j`，所以我们手动维护这个 `prefix` 子串，避免调用子串截取函数，进一步提高效率。这个小优化就留给你来做吧。
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<a href="https://labuladong.online/algo-visualize/leetcode/word-break/" target="_blank">
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这个解法能够通过所有测试用例，我们根据 [算法时空复杂度使用指南](https://labuladong.online/algo/essential-technique/complexity-analysis/) 来算一下它的时间复杂度：
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<a href="https://labuladong.online/algo-visualize/leetcode/word-break-ii/" target="_blank">
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这个解法依然用备忘录消除了重叠子问题，所以 `dp` 函数递归调用的次数减少为 $O(N)$，但 `dp` 函数本身的时间复杂度上升了，因为 `subProblem` 是一个子集列表，它的长度是指数级的。
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‎动态规划系列/团灭股票问题.md‎

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<visual slug="best-time-to-buy-and-sell-stock" />
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<a href="https://labuladong.online/algo-visualize/leetcode/best-time-to-buy-and-sell-stock/" target="_blank">
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两种方式都是一样的，不过这种编程方法简洁很多，但是如果没有前面状态转移方程的引导，是肯定看不懂的。后续的题目，你可以对比一下如何把 `dp` 数组的空间优化掉。
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### 309. 最佳买卖股票时机含冷冻期
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<strong>🌈 代码可视化动画🌈</strong>
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### 714. 买卖股票的最佳时机含手续费
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<strong>🍭 代码可视化动画🍭</strong>
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‎动态规划系列/子序列问题模板.md‎

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<a href="https://labuladong.online/algo-visualize/leetcode/longest-palindromic-subsequence/" target="_blank">
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<strong>🥳 代码可视化动画🥳</strong>
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