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Np

As explained in How Large Language Models Work

NP is the complexity class associated with problems whose solutions can be verified in polynomial time by a nondeterministic computation.

NP is a complexity class for problems whose proposed solutions can be verified in polynomial time by a nondeterministic computation. It helps describe why some problems remain difficult even when a system can produce plausible answers. section 7.4

Computational complexity tracks how runtime grows as input grows. Polynomial growth can become expensive, while exponential growth becomes impractical quickly; approximate answers may sometimes avoid the cost of exact solutions. section 7.4

How How Large Language Models Work covers it

How deep it goes
Explains input-size growth, Big-O notation, and approximate solutions, but does not derive NP formally.
Examples
Uses prime factorization, traveling salesperson routing, and resource-allocation problems to illustrate rapidly growing runtimes.
What it assumes
Starts from input size and runtime before introducing Big-O notation and complexity classes.
What you can do after
Evaluate whether exact computation is practical and recognize when approximation may be useful.
How it connects
Connects NP with computational complexity, Big-O notation, LLMs, and approximate solutions.
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This explainer was generated by Manning's knowledge graph from the text of How Large Language Models Work. The section links point to the parts of the book it draws on.