Algorithmic Complexity Explained
Richard explains polynomial time algorithms, emphasizing their efficiency in terms of computational steps relative to input size. He highlights the distinction between polynomial algorithms and their more complex counterparts, such as NP complete and NP hard problems, while discussing the practical implications of algorithm performance in real-world applications. The conversation underscores the significance of optimizing algorithms from quadratic to linear time for handling large datasets effectively.In this clip
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Lex Fridman Podcast
Richard Karp: Algorithms and Computational Complexity | Lex Fridman Podcast #111
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