This document discusses two topics: dynamic programming and optimal binary search trees. Dynamic programming provides an efficient O(mn) algorithm for finding the length of the longest common subsequence, which is better than the naive recursive solution. It also notes that the algorithm only returns the length and to see how to print the actual subsequence, refer to the textbook. The second topic discusses optimal binary search trees, which finds the structure of a binary search tree that results in the minimum average cost of searches and updates.

1. Advanced Design and Analysis Techniques
CH 15 - DYNAMIC PROGRAMMING
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10. • Time Complexity of the above
implementation is O(mn) which is
much better than the worst case time
complexity of Naive Recursive
implementation.
• This algorithm/code returns only
length of LCS. Please see the
algorithm for printing the LCS in the
textbook.
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12. Optimal Binary Search
Trees (BSTs)
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25. End
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