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We show that if SAT does not have small circuits, then there must exist a small number of formulas such that every small circuit fails to compute satisfiability correctly on at least one of these formulas. We use this result to show that ifPNP[1℄ = PNP[2℄, then the polynomial-time hierarchy collapses to Sp2 p2 \ p2. Even showing that the hierarchy collapsed… (More)

- Lance Fortnow, John M. Hitchcock, Aduri Pavan, N. V. Vinodchandran, Fengming Wang
- Inf. Comput.
- 2005

We apply results on extracting randomness from independent sources to “extract” Kolmogorov complexity. For any α, > 0, given a string x with K(x) > α|x|, we show how to use a constant number of advice bits to efficiently compute another string y, |y| = Ω(|x|), with K(y) > (1− )|y|. This result holds for both unbounded and space-bounded Kolmogorov… (More)

- Aduri Pavan, Alan L. Selman
- SIAM J. Comput.
- 2001

We use hypotheses of structural complexity theory to separate various NP-completeness notions. In particular, we introduce an hypothesis from which we describe a set in NP that is T-complete but not P tt -complete. We provide fairly thorough analyses of the hypotheses that we introduce.

- Jin-Yi Cai, Aduri Pavan, D. Sivakumar
- STACS
- 1999

We prove that if there is a polynomial time algorithm which computes the permanent of a matrix of order n for any inverse polynomial fraction of all inputs, then there is a BPP algorithm computing the permanent for every matrix. It follows that this hypothesis implies P #P = BPP. Our algorithm works over any suuciently large nite eld (polynomially larger… (More)

- John M. Hitchcock, Aduri Pavan
- computational complexity
- 2004

We consider hypotheses about nondeterministic computation that have been studied in different contexts and shown to have interesting consequences: The measure hypothesis: NP does not have p-measure 0. The pseudo-NP hypothesis: there is an NP language that can be distinguished from any DTIME $$(2^{{n}^\epsilon})$$ language by an NP refuter. The NP-machine… (More)

We show the following results regarding complete sets. • NP-complete sets and PSPACE-complete sets are many-one autoreducible. • Complete sets of any level of PH, MODPH, or the Boolean hierarchy over NP are many-one autoreducible. • EXP-complete sets are many-one mitotic. • NEXP-complete sets are weakly many-one mitotic. • PSPACE-complete sets are weakly… (More)

- Aduri Pavan, Raghunath Tewari, N. V. Vinodchandran
- computational complexity
- 2010

We report progress on the NL versus UL problem. We show that counting the number of s-t paths in graphs where the number of s-v paths for any v is bounded by a polynomial can be done in FUL: the unambiguous log-space function class. Several new upper bounds follow from this including $${{{ReachFewL} \subseteq {UL}}}$$ and $${{{LFew} \subseteq… (More)

- Aduri Pavan, Alan L. Selman
- STACS
- 2002

We prove that if for some ǫ > 0, NP contains a set that is DTIME(2n ǫ )-bi-immune, then NP contains a set that is 2-Turing complete for NP (hence 3-truth-table complete) but not 1-truth-table complete for NP. Thus this hypothesis implies a strong separation of completeness notions for NP. Lutz and Mayordomo [LM96] and Ambos-Spies and Bentzien [ASB00]… (More)

- John M. Hitchcock, Aduri Pavan
- Electronic Colloquium on Computational Complexity
- 2006

Under the assumption that NP does not have p-measure 0, we investigate reductions to NP-complete sets and prove the following: 1. Adaptive reductions are more powerful than nonadaptive reductions: there is a problem that is Turing-complete for NP but not truth-table-complete. 2. Strong nondeterministic reductions are more powerful than deterministic… (More)

- Tatsuya Imai, Kotaro Nakagawa, Aduri Pavan, N. V. Vinodchandran, Osamu Watanabe
- 2013 IEEE Conference on Computational Complexity
- 2013

We show that the reachability problem over directed planar graphs can be solved simultaneously in polynomial time and approximately O(√n) space. In contrast, the best space bound known for the reachability problem on general directed graphs with polynomial running time is O(n/2√<sup>log n</sup>).