162 research outputs found
Improved Parameterized Algorithms for Constraint Satisfaction
For many constraint satisfaction problems, the algorithm which chooses a
random assignment achieves the best possible approximation ratio. For instance,
a simple random assignment for {\sc Max-E3-Sat} allows 7/8-approximation and
for every \eps >0 there is no polynomial-time (7/8+\eps)-approximation
unless P=NP. Another example is the {\sc Permutation CSP} of bounded arity.
Given the expected fraction of the constraints satisfied by a random
assignment (i.e. permutation), there is no (\rho+\eps)-approximation
algorithm for every \eps >0, assuming the Unique Games Conjecture (UGC).
In this work, we consider the following parameterization of constraint
satisfaction problems. Given a set of constraints of constant arity, can we
satisfy at least constraint, where is the expected fraction
of constraints satisfied by a random assignment? {\sc Constraint Satisfaction
Problems above Average} have been posed in different forms in the literature
\cite{Niedermeier2006,MahajanRamanSikdar09}. We present a faster parameterized
algorithm for deciding whether equations can be simultaneously
satisfied over . As a consequence, we obtain -variable
bikernels for {\sc boolean CSPs} of arity for every fixed , and for {\sc
permutation CSPs} of arity 3. This implies linear bikernels for many problems
under the "above average" parameterization, such as {\sc Max--Sat}, {\sc
Set-Splitting}, {\sc Betweenness} and {\sc Max Acyclic Subgraph}. As a result,
all the parameterized problems we consider in this paper admit -time
algorithms.
We also obtain non-trivial hybrid algorithms for every Max -CSP: for every
instance , we can either approximate beyond the random assignment
threshold in polynomial time, or we can find an optimal solution to in
subexponential time.Comment: A preliminary version of this paper has been accepted for IPEC 201
Random subcubes as a toy model for constraint satisfaction problems
We present an exactly solvable random-subcube model inspired by the structure
of hard constraint satisfaction and optimization problems. Our model reproduces
the structure of the solution space of the random k-satisfiability and
k-coloring problems, and undergoes the same phase transitions as these
problems. The comparison becomes quantitative in the large-k limit. Distance
properties, as well the x-satisfiability threshold, are studied. The model is
also generalized to define a continuous energy landscape useful for studying
several aspects of glassy dynamics.Comment: 21 pages, 4 figure
Parameterized Constraint Satisfaction Problems: a Survey
We consider constraint satisfaction problems parameterized above or below guaranteed values. One example is MaxSat parameterized above m/2: given a CNF formula F with m clauses, decide whether there is a truth assignment that satisfies at least m/2 + k clauses, where k is the parameter. Among other problems we deal with are MaxLin2-AA (given a system of linear equations over F_2 in which each equation has a positive integral weight, decide whether there is an assignment to the variables that satisfies equations of total weight at least W/2+k, where W is the total weight of all equations), Max-r-Lin2-AA (the same as MaxLin2-AA, but each equation has at most r variables, where r is a constant) and Max-r-Sat-AA (given a CNF formula F with m clauses in which each clause has at most r literals, decide whether there is a truth assignment satisfying at least sum_{i=1}^m (1-2^{r_i})+k clauses, where k is the parameter, r_i is the number of literals in clause i, and r is a constant). We also consider Max-r-CSP-AA, a natural generalization of both Max-r-Lin2-AA and Max-r-Sat-AA, order (or, permutation) constraint satisfaction problems parameterized above the average value and some other problems related to MaxSat. We discuss results, both polynomial kernels and parameterized algorithms, obtained for the problems mainly in the last few years as well as some open questions
Sensor networks and distributed CSP: communication, computation and complexity
We introduce SensorDCSP, a naturally distributed benchmark based on a real-world application that arises in the context of networked distributed systems. In order to study the performance of Distributed CSP (DisCSP) algorithms in a truly distributed setting, we use a discrete-event network simulator, which allows us to model the impact of different network traffic conditions on the performance of the algorithms. We consider two complete DisCSP algorithms: asynchronous backtracking (ABT) and asynchronous weak commitment search (AWC), and perform performance comparison for these algorithms on both satisfiable and unsatisfiable instances of SensorDCSP. We found that random delays (due to network traffic or in some cases actively introduced by the agents) combined with a dynamic decentralized restart strategy can improve the performance of DisCSP algorithms. In addition, we introduce GSensorDCSP, a plain-embedded version of SensorDCSP that is closely related to various real-life dynamic tracking systems. We perform both analytical and empirical study of this benchmark domain. In particular, this benchmark allows us to study the attractiveness of solution repairing for solving a sequence of DisCSPs that represent the dynamic tracking of a set of moving objects.This work was supported in part by AFOSR (F49620-01-1-0076, Intelligent Information Systems Institute and MURI F49620-01-1-0361), CICYT (TIC2001-1577-C03-03 and TIC2003-00950), DARPA (F30602-00-2- 0530), an NSF CAREER award (IIS-9734128), and an Alfred P. Sloan Research Fellowship. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the US Government
Solving Hard Computational Problems Efficiently: Asymptotic Parametric Complexity 3-Coloring Algorithm
Many practical problems in almost all scientific and technological
disciplines have been classified as computationally hard (NP-hard or even
NP-complete). In life sciences, combinatorial optimization problems frequently
arise in molecular biology, e.g., genome sequencing; global alignment of
multiple genomes; identifying siblings or discovery of dysregulated pathways.In
almost all of these problems, there is the need for proving a hypothesis about
certain property of an object that can be present only when it adopts some
particular admissible structure (an NP-certificate) or be absent (no admissible
structure), however, none of the standard approaches can discard the hypothesis
when no solution can be found, since none can provide a proof that there is no
admissible structure. This article presents an algorithm that introduces a
novel type of solution method to "efficiently" solve the graph 3-coloring
problem; an NP-complete problem. The proposed method provides certificates
(proofs) in both cases: present or absent, so it is possible to accept or
reject the hypothesis on the basis of a rigorous proof. It provides exact
solutions and is polynomial-time (i.e., efficient) however parametric. The only
requirement is sufficient computational power, which is controlled by the
parameter . Nevertheless, here it is proved that the
probability of requiring a value of to obtain a solution for a
random graph decreases exponentially: , making
tractable almost all problem instances. Thorough experimental analyses were
performed. The algorithm was tested on random graphs, planar graphs and
4-regular planar graphs. The obtained experimental results are in accordance
with the theoretical expected results.Comment: Working pape
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