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Train marshalling is the process of reordering the railcars of a train in such a way that the railcars with the same destination appear consecutively in the final, reassembled train. The process takes place in the shunting yard by means of a number of classification tracks. In the train marshalling problem (TMP), the objective is to perform this rearrangement of the railcars with the use of as few classification tracks as possible. The problem has been shown to be NP-hard, and several exact and approximation algorithms have been developed for it. In this paper, we propose a novel exact dynamic programming (DP) algorithm for the TMP. The worst-case time complexity of this algorithm (which is exponential in the number of destinations and linear in the number of railcars) is lower than that of the best presently available algorithm for the problem, which is an inclusion–exclusion-based DP algorithm. In practice, the proposed algorithm can provide a substantially improved performance compared to its inclusion–exclusion-based counterpart, as demonstrated by the experimental results. [ABSTRACT FROM AUTHOR]

An effective branch-and-bound (B&B) algorithm for exhaustively enumerating the elementary trapping sets (ETSs) in an arbitrary given Tanner graph is described. Given a Tanner graph G and a positive integer \nu , we introduce a novel 0–1 integer linear programming (ILP) formulation of the \mathcalNP -hard problem of finding the minimum \omega for which there exists an $(\omega ,\nu )$ -ETS in $G$ . The B&B procedure is then based on the LP relaxation of this 0–1 ILP formulation. Our novel 0–1 ILP description of the problem yields a strong (tight) LP relaxation, which allows the search space to be pruned very effectively, as confirmed by experimental results. An obvious advantage of the proposed approach is that it does not require the input Tanner graph to be of a particular form (e.g., variable-regular). [ABSTRACT FROM PUBLISHER]

A generator matrix (GM)-based approach for adaptively deriving cut-inducing redundant parity checks (RPCs) during the adaptive linear programming decoding algorithm is presented. More precisely speaking, if the decoder gets stuck in a non-integral pseudocodeword, then the resulting RPCs are likely to provide violated forbidden-set inequalities that can separate this non-integral optimal solution from the feasible region. The described approach can be viewed as a GM-based counterpart of the approach proposed by Zhang and Siegel in 2012. For binary linear codes of low rate, while providing the same error-correcting performance, our approach requires much less computational time compared to its analogue. [ABSTRACT FROM PUBLISHER]

Error-rate floor phenomenon is known to be a serious impediment to the use of low-density parity-check (LDPC) codes for some practical applications that demand high data reliability. In the case of binary erasure channels (BECs), certain error-prone patterns, known as stopping sets, are proven to cause this performance degradation. A possible approach to diminish this drawback over BECs is to eliminate stopping sets by parity-check matrix extension. Given a parity-check matrix H, and a list \mathcal{L} of its stopping sets, we present an integer linear programming (ILP) formulation to find a parity-check equation which eliminates the maximum number of stopping sets in \mathcal{L} . One of the distinguishing advantages of the proposed scheme is its flexibility for modifications such as: limiting the weight of the new parity-check row, making the new row redundant or linearly independent, 4-cycle avoidance, and taking into account the sizes of stopping sets. Armed with these adjustments, the method can provide good performance improvements, as evidenced by simulation results. Furthermore, for a given \varrho\in\mathbbN , by extending the basic formulation, we provide an ILP formulation for finding a set of size \varrho of parity-check equations which can best eliminate the stopping sets in \mathcalL, among all such sets. [ABSTRACT FROM PUBLISHER]

This paper presents a novel method for constructing structured regular low-density parity-check (LDPC) codes based on a special type of combinatorial designs, known as Steiner systems. This code design approach can be considered as a generalization of the well-known method which uses the point-block incidence matrix of a Steiner 2-design for the code construction. Though the given method can be applied on any Steiner system S(t, k, v), in this paper we focus only on Steiner systems with t ≥ 3. Furthermore, we show that not only a Steiner system (X,B) itself, but also its residual design with respect to an arbitrary point x∈ X can be employed for code construction. We also present a technique for constructing binary and non-binary QC-LDPC codes from Steiner systems. The Tanner graph of the constructed codes is free of 4-cycles and hence the codes have girth at least six. Simulation results show that the so constructed codes perform well over the AWGN channel with iterative message-passing decoding. [ABSTRACT FROM AUTHOR]

Abstract: Human haplotypes include essential information about SNPs, which in turn provide valuable information for such studies as finding relationships between some diseases and their potential genetic causes, e.g., for Genome Wide Association Studies. Due to expensiveness of directly determining haplotypes and recent progress in high throughput sequencing, there has been an increasing motivation for haplotype assembly, which is the problem of finding a pair of haplotypes from a set of aligned fragments. Although the problem has been extensively studied and a number of algorithms have already been proposed for the problem, more accurate methods are still beneficial because of high importance of the haplotypes information. In this paper, first, we develop a probabilistic model, that incorporates the Minor Allele Frequency (MAF) of SNP sites, which is missed in the existing maximum likelihood models. Then, we show that the probabilistic model will reduce to the Minimum Error Correction (MEC) model when the information of MAF is omitted and some approximations are made. This result provides a novel theoretical support for the MEC, despite some criticisms against it in the recent literature. Next, under the same approximations, we simplify the model to an extension of the MEC in which the information of MAF is used. Finally, we extend the haplotype assembly algorithm HapSAT by developing a weighted Max-SAT formulation for the simplified model, which is evaluated empirically with positive results. [Copyright &y& Elsevier]