215 research outputs found
Error Correcting Coding for a Non-symmetric Ternary Channel
Ternary channels can be used to model the behavior of some memory devices,
where information is stored in three different levels. In this paper, error
correcting coding for a ternary channel where some of the error transitions are
not allowed, is considered. The resulting channel is non-symmetric, therefore
classical linear codes are not optimal for this channel. We define the
maximum-likelihood (ML) decoding rule for ternary codes over this channel and
show that it is complex to compute, since it depends on the channel error
probability. A simpler alternative decoding rule which depends only on code
properties, called \da-decoding, is then proposed. It is shown that
\da-decoding and ML decoding are equivalent, i.e., \da-decoding is optimal,
under certain conditions. Assuming \da-decoding, we characterize the error
correcting capabilities of ternary codes over the non-symmetric ternary
channel. We also derive an upper bound and a constructive lower bound on the
size of codes, given the code length and the minimum distance. The results
arising from the constructive lower bound are then compared, for short sizes,
to optimal codes (in terms of code size) found by a clique-based search. It is
shown that the proposed construction method gives good codes, and that in some
cases the codes are optimal.Comment: Submitted to IEEE Transactions on Information Theory. Part of this
work was presented at the Information Theory and Applications Workshop 200
Simple Local Computation Algorithms for the General Lovasz Local Lemma
We consider the task of designing Local Computation Algorithms (LCA) for
applications of the Lov\'{a}sz Local Lemma (LLL). LCA is a class of sublinear
algorithms proposed by Rubinfeld et al.~\cite{Ronitt} that have received a lot
of attention in recent years. The LLL is an existential, sufficient condition
for a collection of sets to have non-empty intersection (in applications,
often, each set comprises all objects having a certain property). The
ground-breaking algorithm of Moser and Tardos~\cite{MT} made the LLL fully
constructive, following earlier results by Beck~\cite{beck_lll} and
Alon~\cite{alon_lll} giving algorithms under significantly stronger LLL-like
conditions. LCAs under those stronger conditions were given in~\cite{Ronitt},
where it was asked if the Moser-Tardos algorithm can be used to design LCAs
under the standard LLL condition. The main contribution of this paper is to
answer this question affirmatively. In fact, our techniques yield LCAs for
settings beyond the standard LLL condition
Two genetic algorithms for the bandwidth multicoloring problem
In this paper the Bandwidth Multicoloring Problem (BMCP) and the Bandwidth Coloring Problem (BCP) are considered. The problems are solved by two genetic algorithms (GAs) which use the integer encoding and standard genetic operators adapted to the problems. In both proposed implementations, all individuals are feasible by default, so search is directed into the promising regions. The first proposed method named GA1 is a constructive metaheuristic that construct solution, while the second named GA2 is an improving metaheuristic used to improve an existing solution. Genetic algorithms are tested on the publicly-available GEOM instances from the literature. Proposed GA1 has achieved a much better solution than the calculated upper bound for a given problem, and GA2 has significantly improved the solutions obtained by GA1. The obtained results are also compared with the results of the existing methods for solving BCP and BMCP
Ordering heuristics for parallel graph coloring
This paper introduces the largest-log-degree-first (LLF) and smallest-log-degree-last (SLL) ordering heuristics for paral-lel greedy graph-coloring algorithms, which are inspired by the largest-degree-first (LF) and smallest-degree-last (SL) serial heuristics, respectively. We show that although LF and SL, in prac-tice, generate colorings with relatively small numbers of colors, they are vulnerable to adversarial inputs for which any paralleliza-tion yields a poor parallel speedup. In contrast, LLF and SLL allow for provably good speedups on arbitrary inputs while, in practice, producing colorings of competitive quality to their serial analogs. We applied LLF and SLL to the parallel greedy coloring algo-rithm introduced by Jones and Plassmann, referred to here as JP. Jones and Plassman analyze the variant of JP that processes the ver-tices of a graph in a random order, and show that on an O(1)-degree graph G = (V,E), this JP-R variant has an expected parallel run-ning time of O(lgV / lg lgV) in a PRAM model. We improve this bound to show, using work-span analysis, that JP-R, augmented to handle arbitrary-degree graphs, colors a graph G = (V,E) with degree â using Î(V +E) work and O(lgV + lg â ·min{âE,â+ lg â lgV / lg lgV}) expected span. We prove that JP-LLF and JP-SLL â JP using the LLF and SLL heuristics, respectively â execute with the same asymptotic work as JP-R and only logarith-mically more span while producing higher-quality colorings than JP-R in practice. We engineered an efficient implementation of JP for modern shared-memory multicore computers and evaluated its performance on a machine with 12 Intel Core-i7 (Nehalem) processor cores. Our implementation of JP-LLF achieves a geometric-mean speedup of 7.83 on eight real-world graphs and a geometric-mean speedup of 8.08 on ten synthetic graphs, while our implementation using SLL achieves a geometric-mean speedup of 5.36 on these real-world graphs and a geometric-mean speedup of 7.02 on these synthetic graphs. Furthermore, on one processor, JP-LLF is slightly faster than a well-engineered serial greedy algorithm using LF, and like-wise, JP-SLL is slightly faster than the greedy algorithm using SL
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