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Commit fa2514d

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add visualization
1 parent b5ed31b commit fa2514d

33 files changed

+802
-82
lines changed

‎动态规划系列/LCS.md

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```
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<a href="https://labuladong.online/algo-visualize/leetcode/longest-common-subsequence/" target="_blank">
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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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‎动态规划系列/动态规划之正则表达.md

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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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<a href="https://labuladong.online/algo-visualize/tutorial/mydata-lis/" target="_blank">
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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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‎动态规划系列/动态规划详解进阶.md

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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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<a href="https://labuladong.online/algo-visualize/tutorial/mydata-fib3/" target="_blank">
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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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<a href="https://labuladong.online/algo-visualize/leetcode/coin-change/" target="_blank">
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‎动态规划系列/单词拼接.md

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> [!TIP]
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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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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),ドル但 `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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<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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### 714. 买卖股票的最佳时机含手续费
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‎动态规划系列/子序列问题模板.md

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