10 research outputs found
Unconditional Lower Bounds against Advice
We show several unconditional lower bounds for exponential time classes against polynomial time classes with advice, including: 1. For any constant c, NEXP ̸ ⊆ P NP[nc
Consistency of circuit lower bounds with bounded theories
Proving that there are problems in 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 . 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 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 it is
consistent with theory that computational class , where is one of
the pairs: and , and , and
. In other words, these theories cannot establish
infinitely often circuit upper bounds for the corresponding problems. This is
of interest because the weaker theory 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
Robust Simulations and Significant Separations
We define and study a new notion of "robust simulations" between complexity
classes which is intermediate between the traditional notions of
infinitely-often and almost-everywhere, as well as a corresponding notion of
"significant separations". A language L has a robust simulation in a complexity
class C if there is a language in C which agrees with L on arbitrarily large
polynomial stretches of input lengths. There is a significant separation of L
from C if there is no robust simulation of L in C. The new notion of simulation
is a cleaner and more natural notion of simulation than the infinitely-often
notion. We show that various implications in complexity theory such as the
collapse of PH if NP = P and the Karp-Lipton theorem have analogues for robust
simulations. We then use these results to prove that most known separations in
complexity theory, such as hierarchy theorems, fixed polynomial circuit lower
bounds, time-space tradeoffs, and the theorems of Allender and Williams, can be
strengthened to significant separations, though in each case, an almost
everywhere separation is unknown.
Proving our results requires several new ideas, including a completely
different proof of the hierarchy theorem for non-deterministic polynomial time
than the ones previously known
Algebraic Methods in Computational Complexity
From 11.10. to 16.10.2009, the Dagstuhl Seminar 09421 “Algebraic Methods in Computational Complexity “ was held in Schloss Dagstuhl-Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available