2,735 research outputs found

    A New Push-Relabel Algorithm for Sparse Networks

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    In this paper, we present a new push-relabel algorithm for the maximum flow problem on flow networks with nn vertices and mm arcs. Our algorithm computes a maximum flow in O(mn)O(mn) time on sparse networks where m=O(n)m = O(n). To our knowledge, this is the first O(mn)O(mn) time push-relabel algorithm for the m=O(n)m = O(n) edge case; previously, it was known that push-relabel implementations could find a max-flow in O(mn)O(mn) time when m=Ω(n1+ϵ)m = \Omega(n^{1+\epsilon}) (King, et. al., SODA `92). This also matches a recent flow decomposition-based algorithm due to Orlin (STOC `13), which finds a max-flow in O(mn)O(mn) time on sparse networks. Our main result is improving on the Excess-Scaling algorithm (Ahuja & Orlin, 1989) by reducing the number of nonsaturating pushes to O(mn)O(mn) across all scaling phases. This is reached by combining Ahuja and Orlin's algorithm with Orlin's compact flow networks. A contribution of this paper is demonstrating that the compact networks technique can be extended to the push-relabel family of algorithms. We also provide evidence that this approach could be a promising avenue towards an O(mn)O(mn)-time algorithm for all edge densities.Comment: 23 pages. arXiv admin note: substantial text overlap with arXiv:1309.2525 - This version includes an extension of the result to the O(n) edge cas

    Improved Compact Visibility Representation of Planar Graph via Schnyder's Realizer

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    Let GG be an nn-node planar graph. In a visibility representation of GG, each node of GG is represented by a horizontal line segment such that the line segments representing any two adjacent nodes of GG are vertically visible to each other. In the present paper we give the best known compact visibility representation of GG. Given a canonical ordering of the triangulated GG, our algorithm draws the graph incrementally in a greedy manner. We show that one of three canonical orderings obtained from Schnyder's realizer for the triangulated GG yields a visibility representation of GG no wider than 22n−4015\frac{22n-40}{15}. Our easy-to-implement O(n)-time algorithm bypasses the complicated subroutines for four-connected components and four-block trees required by the best previously known algorithm of Kant. Our result provides a negative answer to Kant's open question about whether 3n−62\frac{3n-6}{2} is a worst-case lower bound on the required width. Also, if GG has no degree-three (respectively, degree-five) internal node, then our visibility representation for GG is no wider than 4n−93\frac{4n-9}{3} (respectively, 4n−73\frac{4n-7}{3}). Moreover, if GG is four-connected, then our visibility representation for GG is no wider than n−1n-1, matching the best known result of Kant and He. As a by-product, we obtain a much simpler proof for a corollary of Wagner's Theorem on realizers, due to Bonichon, Sa\"{e}c, and Mosbah.Comment: 11 pages, 6 figures, the preliminary version of this paper is to appear in Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science (STACS), Berlin, Germany, 200

    Chain Reduction for Binary and Zero-Suppressed Decision Diagrams

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    Chain reduction enables reduced ordered binary decision diagrams (BDDs) and zero-suppressed binary decision diagrams (ZDDs) to each take advantage of the others' ability to symbolically represent Boolean functions in compact form. For any Boolean function, its chain-reduced ZDD (CZDD) representation will be no larger than its ZDD representation, and at most twice the size of its BDD representation. The chain-reduced BDD (CBDD) of a function will be no larger than its BDD representation, and at most three times the size of its CZDD representation. Extensions to the standard algorithms for operating on BDDs and ZDDs enable them to operate on the chain-reduced versions. Experimental evaluations on representative benchmarks for encoding word lists, solving combinatorial problems, and operating on digital circuits indicate that chain reduction can provide significant benefits in terms of both memory and execution time

    Universality and diversity of folding mechanics for three-helix bundle proteins

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    In this study we evaluate, at full atomic detail, the folding processes of two small helical proteins, the B domain of protein A and the Villin headpiece. Folding kinetics are studied by performing a large number of ab initio Monte Carlo folding simulations using a single transferable all-atom potential. Using these trajectories, we examine the relaxation behavior, secondary structure formation, and transition-state ensembles (TSEs) of the two proteins and compare our results with experimental data and previous computational studies. To obtain a detailed structural information on the folding dynamics viewed as an ensemble process, we perform a clustering analysis procedure based on graph theory. Moreover, rigorous pfold analysis is used to obtain representative samples of the TSEs and a good quantitative agreement between experimental and simulated Fi-values is obtained for protein A. Fi-values for Villin are also obtained and left as predictions to be tested by future experiments. Our analysis shows that two-helix hairpin is a common partially stable structural motif that gets formed prior to entering the TSE in the studied proteins. These results together with our earlier study of Engrailed Homeodomain and recent experimental studies provide a comprehensive, atomic-level picture of folding mechanics of three-helix bundle proteins.Comment: PNAS, in press, revised versio

    Identifying gene locus associations with promyelocytic leukemia nuclear bodies using immuno-TRAP.

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    Important insights into nuclear function would arise if gene loci physically interacting with particular subnuclear domains could be readily identified. Immunofluorescence microscopy combined with fluorescence in situ hybridization (immuno-FISH), the method that would typically be used in such a study, is limited by spatial resolution and requires prior assumptions for selecting genes to probe. Our new technique, immuno-TRAP, overcomes these limitations. Using promyelocytic leukemia nuclear bodies (PML NBs) as a model, we used immuno-TRAP to determine if specific genes localize within molecular dimensions with these bodies. Although we confirmed a TP53 gene-PML NB association, immuno-TRAP allowed us to uncover novel locus-PML NB associations, including the ABCA7 and TFF1 loci and, most surprisingly, the PML locus itself. These associations were cell type specific and reflected the cell's physiological state. Combined with microarrays or deep sequencing, immuno-TRAP provides powerful opportunities for identifying gene locus associations with potentially any nuclear subcompartment
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