3,291 research outputs found
Conjunctive Query Answering for the Description Logic SHIQ
Conjunctive queries play an important role as an expressive query language
for Description Logics (DLs). Although modern DLs usually provide for
transitive roles, conjunctive query answering over DL knowledge bases is only
poorly understood if transitive roles are admitted in the query. In this paper,
we consider unions of conjunctive queries over knowledge bases formulated in
the prominent DL SHIQ and allow transitive roles in both the query and the
knowledge base. We show decidability of query answering in this setting and
establish two tight complexity bounds: regarding combined complexity, we prove
that there is a deterministic algorithm for query answering that needs time
single exponential in the size of the KB and double exponential in the size of
the query, which is optimal. Regarding data complexity, we prove containment in
co-NP
What's Up with Downward Collapse: Using the Easy-Hard Technique to Link Boolean and Polynomial Hierarchy Collapses
During the past decade, nine papers have obtained increasingly strong
consequences from the assumption that boolean or bounded-query hierarchies
collapse. The final four papers of this nine-paper progression actually achieve
downward collapse---that is, they show that high-level collapses induce
collapses at (what beforehand were thought to be) lower complexity levels. For
example, for each it is now known that if \psigkone=\psigktwo then
\ph=\sigmak. This article surveys the history, the results, and the
technique---the so-called easy-hard method---of these nine papers.Comment: 37 pages. an extended abstract appeared in SIGACT News, 29, 10-22,
199
A PRG for Lipschitz Functions of Polynomials with Applications to Sparsest Cut
We give improved pseudorandom generators (PRGs) for Lipschitz functions of
low-degree polynomials over the hypercube. These are functions of the form
psi(P(x)), where P is a low-degree polynomial and psi is a function with small
Lipschitz constant. PRGs for smooth functions of low-degree polynomials have
received a lot of attention recently and play an important role in constructing
PRGs for the natural class of polynomial threshold functions. In spite of the
recent progress, no nontrivial PRGs were known for fooling Lipschitz functions
of degree O(log n) polynomials even for constant error rate. In this work, we
give the first such generator obtaining a seed-length of (log
n)\tilde{O}(d^2/eps^2) for fooling degree d polynomials with error eps.
Previous generators had an exponential dependence on the degree.
We use our PRG to get better integrality gap instances for sparsest cut, a
fundamental problem in graph theory with many applications in graph
optimization. We give an instance of uniform sparsest cut for which a powerful
semi-definite relaxation (SDP) first introduced by Goemans and Linial and
studied in the seminal work of Arora, Rao and Vazirani has an integrality gap
of exp(\Omega((log log n)^{1/2})). Understanding the performance of the
Goemans-Linial SDP for uniform sparsest cut is an important open problem in
approximation algorithms and metric embeddings and our work gives a
near-exponential improvement over previous lower bounds which achieved a gap of
\Omega(log log n)
-Approximation Algorithm for Directed Steiner Tree: A Tight Quasi-Polynomial-Time Algorithm
In the Directed Steiner Tree (DST) problem we are given an -vertex
directed edge-weighted graph, a root , and a collection of terminal
nodes. Our goal is to find a minimum-cost arborescence that contains a directed
path from to every terminal. We present an -approximation algorithm for DST that runs in
quasi-polynomial-time. By adjusting the parameters in the hardness result of
Halperin and Krauthgamer, we show the matching lower bound of
for the class of quasi-polynomial-time
algorithms. This is the first improvement on the DST problem since the
classical quasi-polynomial-time approximation algorithm by
Charikar et al. (The paper erroneously claims an approximation due
to a mistake in prior work.)
Our approach is based on two main ingredients. First, we derive an
approximation preserving reduction to the Label-Consistent Subtree (LCST)
problem. The LCST instance has quasi-polynomial size and logarithmic height. We
remark that, in contrast, Zelikovsky's heigh-reduction theorem used in all
prior work on DST achieves a reduction to a tree instance of the related Group
Steiner Tree (GST) problem of similar height, however losing a logarithmic
factor in the approximation ratio. Our second ingredient is an LP-rounding
algorithm to approximately solve LCST instances, which is inspired by the
framework developed by Rothvo{\ss}. We consider a Sherali-Adams lifting of a
proper LP relaxation of LCST. Our rounding algorithm proceeds level by level
from the root to the leaves, rounding and conditioning each time on a proper
subset of label variables. A small enough (namely, polylogarithmic) number of
Sherali-Adams lifting levels is sufficient to condition up to the leaves
LP/SDP Hierarchy Lower Bounds for Decoding Random LDPC Codes
Random (dv,dc)-regular LDPC codes are well-known to achieve the Shannon
capacity of the binary symmetric channel (for sufficiently large dv and dc)
under exponential time decoding. However, polynomial time algorithms are only
known to correct a much smaller fraction of errors. One of the most powerful
polynomial-time algorithms with a formal analysis is the LP decoding algorithm
of Feldman et al. which is known to correct an Omega(1/dc) fraction of errors.
In this work, we show that fairly powerful extensions of LP decoding, based on
the Sherali-Adams and Lasserre hierarchies, fail to correct much more errors
than the basic LP-decoder. In particular, we show that:
1) For any values of dv and dc, a linear number of rounds of the
Sherali-Adams LP hierarchy cannot correct more than an O(1/dc) fraction of
errors on a random (dv,dc)-regular LDPC code.
2) For any value of dv and infinitely many values of dc, a linear number of
rounds of the Lasserre SDP hierarchy cannot correct more than an O(1/dc)
fraction of errors on a random (dv,dc)-regular LDPC code.
Our proofs use a new stretching and collapsing technique that allows us to
leverage recent progress in the study of the limitations of LP/SDP hierarchies
for Maximum Constraint Satisfaction Problems (Max-CSPs). The problem then
reduces to the construction of special balanced pairwise independent
distributions for Sherali-Adams and special cosets of balanced pairwise
independent subgroups for Lasserre.
Some of our techniques are more generally applicable to a large class of
Boolean CSPs called Min-Ones. In particular, for k-Hypergraph Vertex Cover, we
obtain an improved integrality gap of that holds after a
\emph{linear} number of rounds of the Lasserre hierarchy, for any k = q+1 with
q an arbitrary prime power. The best previous gap for a linear number of rounds
was equal to and due to Schoenebeck.Comment: 23 page
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