2,912 research outputs found

    nested PLS

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    In this note we will introduce a class of search problems, called nested Polynomial Local Search (nPLS) problems, and show that definable NP search problems, i.e., Σ1b\Sigma^b_1-definable functions in T22T^2_2 are characterized in terms of the nested PLS

    A proof of P!=NP

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    We show that it is provable in PA that there is an arithmetically definable sequence {ϕn:nω}\{\phi_{n}:n \in \omega\} of Π20\Pi^{0}_{2}-sentences, such that - PRA+{ϕn:nω}\{\phi_{n}:n \in \omega\} is Π20\Pi^{0}_{2}-sound and Π10\Pi^{0}_{1}-complete - the length of ϕn\phi_{n} is bounded above by a polynomial function of nn with positive leading coefficient - PRA+ϕn+1\phi_{n+1} always proves 1-consistency of PRA+ϕn\phi_{n}. One has that the growth in logical strength is in some sense "as fast as possible", manifested in the fact that the total general recursive functions whose totality is asserted by the true Π20\Pi^{0}_{2}-sentences in the sequence are cofinal growth-rate-wise in the set of all total general recursive functions. We then develop an argument which makes use of a sequence of sentences constructed by an application of the diagonal lemma, which are generalisations in a broad sense of Hugh Woodin's "Tower of Hanoi" construction as outlined in his essay "Tower of Hanoi" in Chapter 18 of the anthology "Truth in Mathematics". The argument establishes the result that it is provable in PA that PNPP \neq NP. We indicate how to pull the argument all the way down into EFA

    Consistency of circuit lower bounds with bounded theories

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    Proving that there are problems in PNP\mathsf{P}^\mathsf{NP} that require boolean circuits of super-linear size is a major frontier in complexity theory. While such lower bounds are known for larger complexity classes, existing results only show that the corresponding problems are hard on infinitely many input lengths. For instance, proving almost-everywhere circuit lower bounds is open even for problems in MAEXP\mathsf{MAEXP}. Giving the notorious difficulty of proving lower bounds that hold for all large input lengths, we ask the following question: Can we show that a large set of techniques cannot prove that NP\mathsf{NP} is easy infinitely often? Motivated by this and related questions about the interaction between mathematical proofs and computations, we investigate circuit complexity from the perspective of logic. Among other results, we prove that for any parameter k1k \geq 1 it is consistent with theory TT that computational class C⊈i.o.SIZE(nk){\mathcal C} \not \subseteq \textit{i.o.}\mathrm{SIZE}(n^k), where (T,C)(T, \mathcal{C}) is one of the pairs: T=T21T = \mathsf{T}^1_2 and C=PNP{\mathcal C} = \mathsf{P}^\mathsf{NP}, T=S21T = \mathsf{S}^1_2 and C=NP{\mathcal C} = \mathsf{NP}, T=PVT = \mathsf{PV} and C=P{\mathcal C} = \mathsf{P}. In other words, these theories cannot establish infinitely often circuit upper bounds for the corresponding problems. This is of interest because the weaker theory PV\mathsf{PV} already formalizes sophisticated arguments, such as a proof of the PCP Theorem. These consistency statements are unconditional and improve on earlier theorems of [KO17] and [BM18] on the consistency of lower bounds with PV\mathsf{PV}

    Theories for TC0 and Other Small Complexity Classes

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    We present a general method for introducing finitely axiomatizable "minimal" two-sorted theories for various subclasses of P (problems solvable in polynomial time). The two sorts are natural numbers and finite sets of natural numbers. The latter are essentially the finite binary strings, which provide a natural domain for defining the functions and sets in small complexity classes. We concentrate on the complexity class TC^0, whose problems are defined by uniform polynomial-size families of bounded-depth Boolean circuits with majority gates. We present an elegant theory VTC^0 in which the provably-total functions are those associated with TC^0, and then prove that VTC^0 is "isomorphic" to a different-looking single-sorted theory introduced by Johannsen and Pollet. The most technical part of the isomorphism proof is defining binary number multiplication in terms a bit-counting function, and showing how to formalize the proofs of its algebraic properties.Comment: 40 pages, Logical Methods in Computer Scienc
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