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Update intro-to-dp.md
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src/dynamic_programming/intro-to-dp.md

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### *** Very important note***
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The layman's way of analyzing the runtime of a memoized recursive function is:
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$$** (work per subproblem) * (number of subproblems) **$$
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$${ \text{work per subproblem} * \text{number of subproblems} }$$
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Using a binary search tree (map in C++) to save states will technically result in $O(n \log n)$ as each lookup and insertion will take $O(\log n)$ work and with $O(n)$ unique subproblems we have $O(n \log n)$ time.
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## Practice Problems
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* [LeetCode - 1137. N-th Tribonacci Number](https://leetcode.com/problems/n-th-tribonacci-number/description/)
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* [LeetCode - 118. Pascal's Triangle](https://leetcode.com/problems/pascals-triangle/description/)
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* [LeetCode - 1025. Divisor Game](https://leetcode.com/problems/divisor-game/description/)
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* [LeetCode - 1025. Divisor Game](https://leetcode.com/problems/divisor-game/description/)

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