68,891 research outputs found
Linear Compressed Pattern Matching for Polynomial Rewriting (Extended Abstract)
This paper is an extended abstract of an analysis of term rewriting where the
terms in the rewrite rules as well as the term to be rewritten are compressed
by a singleton tree grammar (STG). This form of compression is more general
than node sharing or representing terms as dags since also partial trees
(contexts) can be shared in the compression. In the first part efficient but
complex algorithms for detecting applicability of a rewrite rule under
STG-compression are constructed and analyzed. The second part applies these
results to term rewriting sequences.
The main result for submatching is that finding a redex of a left-linear rule
can be performed in polynomial time under STG-compression.
The main implications for rewriting and (single-position or parallel)
rewriting steps are: (i) under STG-compression, n rewriting steps can be
performed in nondeterministic polynomial time. (ii) under STG-compression and
for left-linear rewrite rules a sequence of n rewriting steps can be performed
in polynomial time, and (iii) for compressed rewrite rules where the left hand
sides are either DAG-compressed or ground and STG-compressed, and an
STG-compressed target term, n rewriting steps can be performed in polynomial
time.Comment: In Proceedings TERMGRAPH 2013, arXiv:1302.599
Compression via Matroids: A Randomized Polynomial Kernel for Odd Cycle Transversal
The Odd Cycle Transversal problem (OCT) asks whether a given graph can be
made bipartite by deleting at most of its vertices. In a breakthrough
result Reed, Smith, and Vetta (Operations Research Letters, 2004) gave a
\BigOh(4^kkmn) time algorithm for it, the first algorithm with polynomial
runtime of uniform degree for every fixed . It is known that this implies a
polynomial-time compression algorithm that turns OCT instances into equivalent
instances of size at most \BigOh(4^k), a so-called kernelization. Since then
the existence of a polynomial kernel for OCT, i.e., a kernelization with size
bounded polynomially in , has turned into one of the main open questions in
the study of kernelization.
This work provides the first (randomized) polynomial kernelization for OCT.
We introduce a novel kernelization approach based on matroid theory, where we
encode all relevant information about a problem instance into a matroid with a
representation of size polynomial in . For OCT, the matroid is built to
allow us to simulate the computation of the iterative compression step of the
algorithm of Reed, Smith, and Vetta, applied (for only one round) to an
approximate odd cycle transversal which it is aiming to shrink to size . The
process is randomized with one-sided error exponentially small in , where
the result can contain false positives but no false negatives, and the size
guarantee is cubic in the size of the approximate solution. Combined with an
\BigOh(\sqrt{\log n})-approximation (Agarwal et al., STOC 2005), we get a
reduction of the instance to size \BigOh(k^{4.5}), implying a randomized
polynomial kernelization.Comment: Minor changes to agree with SODA 2012 version of the pape
Pattern matching of compressed terms and contexts and polynomial rewriting
A generalization of the compressed string pattern match that applies to terms with variables is investigated: Given terms s and t compressed by singleton tree grammars, the task is to find an instance of s that occurs as a subterm in t. We show that this problem is in NP and that the task can be performed in time O(ncjVar(s)j), including the construction of the compressed substitution, and a representation of all occurrences. We show that the special case where s is uncompressed can be performed in polynomial time. As a nice application we show that for an equational deduction of t to t0 by an equality axiom l = r (a rewrite) a single step can be performed in polynomial time in the size of compression of t and l; r if the number of variables is fixed in l. We also show that n rewriting steps can be performed in polynomial time, if the equational axioms are compressed and assumed to be constant for the rewriting sequence. Another potential application are querying mechanisms on compressed XML-data bases
Unification on Compressed Terms
First-order term unification is an essential concept in areas like functional and
logic programming, automated deduction, deductive databases, artificial intelligence,
information retrieval, compiler design, etc. We build upon recent
developments in grammar-based compression mechanisms for terms and investigate
algorithms for first-order unification and matching on compressed
terms.
We prove that the first-order unification of compressed terms is decidable
in polynomial time, and also that a compressed representation of the most
general unifier can be computed in polynomial time. Furthermore, we present
a polynomial time algorithm for first-order matching on compressed terms.
Both algorithms represent an improvement in time complexity over previous
results [GGSS09, GGSS08].
We use several known results on the tree grammars used for compression,
called singleton tree grammars (STG)s, like polynomial time computability
of several subalgorithmms: certain grammar extensions, deciding equality of
represented terms, and generating their preorder traversal. An innovation is
a specialized depth of an STG that shows that unifiers can be represented in
polynomial spac
Unification and Matching on Compressed Terms
Term unification plays an important role in many areas of computer science,
especially in those related to logic. The universal mechanism of grammar-based
compression for terms, in particular the so-called Singleton Tree Grammars
(STG), have recently drawn considerable attention. Using STGs, terms of
exponential size and height can be represented in linear space. Furthermore,
the term representation by directed acyclic graphs (dags) can be efficiently
simulated. The present paper is the result of an investigation on term
unification and matching when the terms given as input are represented using
different compression mechanisms for terms such as dags and Singleton Tree
Grammars. We describe a polynomial time algorithm for context matching with
dags, when the number of different context variables is fixed for the problem.
For the same problem, NP-completeness is obtained when the terms are
represented using the more general formalism of Singleton Tree Grammars. For
first-order unification and matching polynomial time algorithms are presented,
each of them improving previous results for those problems.Comment: This paper is posted at the Computing Research Repository (CoRR) as
part of the process of submission to the journal ACM Transactions on
Computational Logic (TOCL)
Optimal Polynomial-Time Compression for Boolean Max CSP
In the Boolean maximum constraint satisfaction problem - Max CSP(?) - one is given a collection of weighted applications of constraints from a finite constraint language ?, over a common set of variables, and the goal is to assign Boolean values to the variables so that the total weight of satisfied constraints is maximized. There exists a concise dichotomy theorem providing a criterion on ? for the problem to be polynomial-time solvable and stating that otherwise it becomes NP-hard. We study the NP-hard cases through the lens of kernelization and provide a complete characterization of Max CSP(?) with respect to the optimal compression size. Namely, we prove that Max CSP(?) parameterized by the number of variables n is either polynomial-time solvable, or there exists an integer d ? 2 depending on ?, such that:
1) An instance of Max CSP(?) can be compressed into an equivalent instance with ?(n^d log n) bits in polynomial time,
2) Max CSP(?) does not admit such a compression to ?(n^{d-?}) bits unless NP ? co-NP / poly.
Our reductions are based on interpreting constraints as multilinear polynomials combined with the framework of constraint implementations. As another application of our reductions, we reveal tight connections between optimal running times for solving Max CSP(?). More precisely, we show that obtaining a running time of the form ?(2^{(1-?)n}) for particular classes of Max CSPs is as hard as breaching this barrier for Max d-SAT for some d
- …