Complexity Classes as Pure Sets

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SUMMARY

This discussion focuses on the definition of complexity classes such as P, NP, NP-Hard, and NP-Complete in the context of pure sets within Zermelo-Fraenkel set theory with the Axiom of Choice (ZFC). The participants emphasize that complexity classes can be represented as sets of decision problems, where P consists of problems solvable by polynomial time algorithms, NP includes problems solvable by non-deterministic polynomial time algorithms, and NP-Hard encompasses problems to which all NP problems can be reduced in polynomial time. The conversation highlights the challenges of defining these sets in elementary set-theoretic terms and suggests that complexity measures must be considered to bridge the gap between abstract definitions and practical computation.

PREREQUISITES
  • Understanding of Zermelo-Fraenkel set theory with the Axiom of Choice (ZFC)
  • Familiarity with complexity classes: P, NP, NP-Hard, NP-Complete
  • Knowledge of Turing machines and decision problems
  • Basic concepts of polynomial time algorithms and non-deterministic algorithms
NEXT STEPS
  • Research the formal definitions of complexity classes in ZFC set theory
  • Explore Turing machine models and their relation to complexity measures
  • Study the implications of polynomial time reductions in computational theory
  • Examine the concept of decision problems and their representations in binary
USEFUL FOR

This discussion is beneficial for computer scientists, mathematicians, and students in theoretical computer science who are interested in the foundations of computational complexity and set theory.

Dragonfall
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What are complexity classes (P, NP, etc) in terms of pure sets? ZFC, I mean.
 
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I thought they were just sets. Like

P = {problem q | there is a polynomial time algorithm for solving q}
NP = {problem q | there is a non-deterministic polynomial time algorithm for solving q}
NP-Hard = {problem q | any problem q' in NP can be reduced to q in polynomial time}
NP-Complete = NP (intersect) NP-Hard

etc.
 
I depends on how you define decision problem, Turing computation, etc. Take any book on computation theory, start with the definition of complexity classes given by AUMathTutor and work backwards by substituting each concept by its definition and eventually you should end up with an ugly expression for that set. Note however that the result is likely to vary depending on our definitions so there is no universal way to express such sets, and I expect that they will be fairly meaningless to humans.
 
AUMathTutor said:
I thought they were just sets. Like

P = {problem q | there is a polynomial time algorithm for solving q}

etc.

"problem q" would also have to be a set.
 
... since when can there not be sets of sets? Am I missing the point?

Are you asking for the definition of these sets in terms of elementary set theoretic notions? Like somebody said, doing that would be counterproductive. I think the way to bridge the gap from this to more basic notions is via the mechanism of language and machine.
 
It's pretty trivial to define them as sets if they're decision problems: just code output in binary for inputs "", "0", "1", "00", "01", "10", "11", "000", ... If they're functions, just code numbers (in a self-delimiting format) rather than bits. Take the resultant binary string and code as the set of the set bits' usual set representations: 1 = {{}}, 2 = {{}, {{}}}, etc.
 
Set as decision problems don't account for complexity. I guess you'd have to define some sort of complexity measure, and the measure would have to depend on the turing machine or whatever formality used. Then define some sort of equivalent class. My head hurts just thinking about it.
 

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