6,804 research outputs found
Formulas vs. Circuits for Small Distance Connectivity
We give the first super-polynomial separation in the power of bounded-depth
boolean formulas vs. circuits. Specifically, we consider the problem Distance
Connectivity, which asks whether two specified nodes in a graph of size
are connected by a path of length at most . This problem is solvable
(by the recursive doubling technique) on {\bf circuits} of depth
and size . In contrast, we show that solving this problem on {\bf
formulas} of depth requires size for all . As corollaries:
(i) It follows that polynomial-size circuits for Distance Connectivity
require depth for all . This matches the
upper bound from recursive doubling and improves a previous lower bound of Beame, Pitassi and Impagliazzo [BIP98].
(ii) We get a tight lower bound of on the size required to
simulate size- depth- circuits by depth- formulas for all and . No lower bound better than
was previously known for any .
Our proof technique is centered on a new notion of pathset complexity, which
roughly speaking measures the minimum cost of constructing a set of (partial)
paths in a universe of size via the operations of union and relational
join, subject to certain density constraints. Half of our proof shows that
bounded-depth formulas solving Distance Connectivity imply upper bounds
on pathset complexity. The other half is a combinatorial lower bound on pathset
complexity
Near-optimal small-depth lower bounds for small distance connectivity
We show that any depth- circuit for determining whether an -node graph
has an -to- path of length at most must have size
. The previous best circuit size lower bounds for this
problem were (due to Beame, Impagliazzo, and Pitassi
[BIP98]) and (following from a recent formula size
lower bound of Rossman [Ros14]). Our lower bound is quite close to optimal,
since a simple construction gives depth- circuits of size
for this problem (and strengthening our bound even to
would require proving that undirected connectivity is not in )
Our proof is by reduction to a new lower bound on the size of small-depth
circuits computing a skewed variant of the "Sipser functions" that have played
an important role in classical circuit lower bounds [Sip83, Yao85, H{\aa}s86].
A key ingredient in our proof of the required lower bound for these Sipser-like
functions is the use of \emph{random projections}, an extension of random
restrictions which were recently employed in [RST15]. Random projections allow
us to obtain sharper quantitative bounds while employing simpler arguments,
both conceptually and technically, than in the previous works [Ajt89, BPU92,
BIP98, Ros14]
An Atypical Survey of Typical-Case Heuristic Algorithms
Heuristic approaches often do so well that they seem to pretty much always
give the right answer. How close can heuristic algorithms get to always giving
the right answer, without inducing seismic complexity-theoretic consequences?
This article first discusses how a series of results by Berman, Buhrman,
Hartmanis, Homer, Longpr\'{e}, Ogiwara, Sch\"{o}ening, and Watanabe, from the
early 1970s through the early 1990s, explicitly or implicitly limited how well
heuristic algorithms can do on NP-hard problems. In particular, many desirable
levels of heuristic success cannot be obtained unless severe, highly unlikely
complexity class collapses occur. Second, we survey work initiated by Goldreich
and Wigderson, who showed how under plausible assumptions deterministic
heuristics for randomized computation can achieve a very high frequency of
correctness. Finally, we consider formal ways in which theory can help explain
the effectiveness of heuristics that solve NP-hard problems in practice.Comment: This article is currently scheduled to appear in the December 2012
issue of SIGACT New
Improved Pseudorandom Generators from Pseudorandom Multi-Switching Lemmas
We give the best known pseudorandom generators for two touchstone classes in
unconditional derandomization: an -PRG for the class of size-
depth- circuits with seed length , and an -PRG for the class of -sparse
polynomials with seed length . These results bring the state of the art for
unconditional derandomization of these classes into sharp alignment with the
state of the art for computational hardness for all parameter settings:
improving on the seed lengths of either PRG would require breakthrough progress
on longstanding and notorious circuit lower bounds.
The key enabling ingredient in our approach is a new \emph{pseudorandom
multi-switching lemma}. We derandomize recently-developed
\emph{multi}-switching lemmas, which are powerful generalizations of
H{\aa}stad's switching lemma that deal with \emph{families} of depth-two
circuits. Our pseudorandom multi-switching lemma---a randomness-efficient
algorithm for sampling restrictions that simultaneously simplify all circuits
in a family---achieves the parameters obtained by the (full randomness)
multi-switching lemmas of Impagliazzo, Matthews, and Paturi [IMP12] and
H{\aa}stad [H{\aa}s14]. This optimality of our derandomization translates into
the optimality (given current circuit lower bounds) of our PRGs for
and sparse polynomials
An Improved Homomorphism Preservation Theorem From Lower Bounds in Circuit Complexity
Previous work of the author [Rossmann\u2708] showed that the Homomorphism Preservation Theorem of classical model theory remains valid when its statement is restricted to finite structures. In this paper, we give a new proof of this result via a reduction to lower bounds in circuit complexity, specifically on the AC0 formula size of the colored subgraph isomorphism problem. Formally, we show the following: if a first-order sentence of quantifier-rank k is preserved under homomorphisms on finite structures, then it is equivalent on finite structures to an existential-positive sentence of quantifier-rank poly(k). Quantitatively, this improves the result of [Rossmann\u2708], where the upper bound on quantifier-rank is a non-elementary function of k
Near-optimal small-depth lower bounds for small distance connectivity
We show that any depth-d circuit for determining whether an n-node graph has an s-to-t path of length at most k must have size nΩ(k1/d/d). The previous best circuit size lower bounds for this problem were nkexp(−O(d)) (due to Beame, Impagliazzo, and Pitassi [BIP98]) and nΩ((log k)/d) (following from a recent formula size lower bound of Rossman [Ros14]). Our lower bound is quite close to optimal, since a simple construction gives depth-d circuits of size nO(k2/d) for this problem (and strengthening our bound even to nkΩ(1/d) would require proving that undirected connectivity is not in NC1.) Our proof is by reduction to a new lower bound on the size of small-depth circuits computing a skewed variant of the “Sipser functions” that have played an important role in classical circuit lower bounds [Sip83, Yao85, H˚as86]. A key ingredient in our proof of the required lower bound for these Sipser-like functions is the use of random projections, an extension of random restrictions which were recently employed in [RST15]. Random projections allow us to obtain sharper quantitative bounds while employing simpler arguments, both conceptually and technically, than in the previous works [Ajt89, BPU92, BIP98, Ros14]
A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data
Deducing the structure of neural circuits is one of the central problems of
modern neuroscience. Recently-introduced calcium fluorescent imaging methods
permit experimentalists to observe network activity in large populations of
neurons, but these techniques provide only indirect observations of neural
spike trains, with limited time resolution and signal quality. In this work we
present a Bayesian approach for inferring neural circuitry given this type of
imaging data. We model the network activity in terms of a collection of coupled
hidden Markov chains, with each chain corresponding to a single neuron in the
network and the coupling between the chains reflecting the network's
connectivity matrix. We derive a Monte Carlo Expectation--Maximization
algorithm for fitting the model parameters; to obtain the sufficient statistics
in a computationally-efficient manner, we introduce a specialized
blockwise-Gibbs algorithm for sampling from the joint activity of all observed
neurons given the observed fluorescence data. We perform large-scale
simulations of randomly connected neuronal networks with biophysically
realistic parameters and find that the proposed methods can accurately infer
the connectivity in these networks given reasonable experimental and
computational constraints. In addition, the estimation accuracy may be improved
significantly by incorporating prior knowledge about the sparseness of
connectivity in the network, via standard L penalization methods.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS303 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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