Dynamic Programming: Longest Common Subsequence

This lecture covers the concept of dynamic programming, focusing on the Longest Common Subsequence problem. It explains how to identify choices and optimal substructure, use top-down or bottom-up approaches efficiently, and provides a detailed explanation of the dynamic programming algorithm. The lecture also delves into matrix-chain multiplication and recursive formulations for finding the longest common subsequence. It concludes with a discussion on the bottom-up approach for solving the Longest Common Subsequence problem and recording the optimal solution using additional arrays.