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Electronic Colloquium on Computational Complexity

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Reports tagged with counting complexity:
TR97-016 | 29th April 1997
Manindra Agrawal, Eric Allender, Samir Datta

On TC^0, AC^0, and Arithmetic Circuits

Continuing a line of investigation that has studied the
function classes #P, #SAC^1, #L, and #NC^1, we study the
class of functions #AC^0. One way to define #AC^0 is as the
class of functions computed by constant-depth polynomial-size
arithmetic circuits of unbounded fan-in addition ... more >>>

TR99-003 | 18th December 1998
Stephen A. Fenner, Frederic Green, Steven Homer, Randall Pruim

Determining Acceptance Possibility for a Quantum Computation is Hard for the Polynomial Hierarchy

It is shown that determining whether a quantum computation
has a non-zero probability of accepting is at least as hard as the
polynomial time hierarchy. This hardness result also applies to
determining in general whether a given quantum basis state appears
with nonzero amplitude in a superposition, or whether a ... more >>>

TR99-008 | 19th March 1999
Eric Allender, Vikraman Arvind, Meena Mahajan

Arithmetic Complexity, Kleene Closure, and Formal Power Series

Revisions: 1 , Comments: 1

The aim of this paper is to use formal power series techniques to
study the structure of small arithmetic complexity classes such as
GapNC^1 and GapL. More precisely, we apply the Kleene closure of
languages and the formal power series operations of inversion and
root ... more >>>

TR02-036 | 30th May 2002
Stephen A. Fenner

PP-lowness and a simple definition of AWPP

We show that the counting classes AWPP and APP [Li 1993] are more robust
than previously thought. Our results identify asufficient condition for
a language to be low for PP, and we show that this condition is at least
as weak as other previously studied criteria. Our results imply that
more >>>

TR08-044 | 2nd April 2008
Miki Hermann, Reinhard Pichler

Complexity of Counting the Optimal Solutions

Following the approach of Hemaspaandra and Vollmer, we can define
counting complexity classes #.C for any complexity class C of decision
problems. In particular, the classes #.Pi_{k}P with k >= 1
corresponding to all levels of the polynomial hierarchy have thus been
studied. However, for a large variety of counting ... more >>>

TR12-142 | 3rd November 2012
Markus Bläser

Noncommutativity makes determinants hard

We consider the complexity of computing the determinant over arbitrary finite-dimensional algebras. We first consider the case that $A$ is fixed. We obtain the following dichotomy: If $A/rad(A)$ is noncommutative, then computing the determinant over $A$ is hard. ``Hard'' here means $\#P$-hard over fields of characteristic $0$ and $ModP_p$-hard over ... more >>>

TR13-048 | 27th March 2013
Jin-Yi Cai, Aaron Gorenstein

Matchgates Revisited

We study a collection of concepts and theorems that laid the foundation of matchgate computation. This includes the signature theory of planar matchgates, and the parallel theory of characters of not necessarily planar matchgates. Our aim is to present a unified and, whenever possible, simplified account of this challenging theory. ... more >>>

TR15-114 | 18th July 2015
Avishay Tal

#SAT Algorithms from Shrinkage

We present a deterministic algorithm that counts the number of satisfying assignments for any de Morgan formula $F$ of size at most $n^{3-16\epsilon}$ in time $2^{n-n^{\epsilon}}\cdot \mathrm{poly}(n)$, for any small constant $\epsilon>0$. We do this by derandomizing the randomized algorithm mentioned by Komargodski et al. (FOCS, 2013) and Chen et ... more >>>

TR16-002 | 18th January 2016
Ryan Williams

Strong ETH Breaks With Merlin and Arthur: Short Non-Interactive Proofs of Batch Evaluation

We present an efficient proof system for Multipoint Arithmetic Circuit Evaluation: for every arithmetic circuit $C(x_1,\ldots,x_n)$ of size $s$ and degree $d$ over a field ${\mathbb F}$, and any inputs $a_1,\ldots,a_K \in {\mathbb F}^n$,
$\bullet$ the Prover sends the Verifier the values $C(a_1), \ldots, C(a_K) \in {\mathbb F}$ and ... more >>>

TR23-082 | 1st June 2023
Ryan Williams

Self-Improvement for Circuit-Analysis Problems

Many results in fine-grained complexity reveal intriguing consequences from solving various SAT problems even slightly faster than exhaustive search. We prove a ``self-improving'' (or ``bootstrapping'') theorem for Circuit-SAT, $\#$Circuit-SAT, and its fully-quantified version: solving one of these problems faster for ``large'' circuit sizes implies a significant speed-up for ``smaller'' circuit ... more >>>

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