93 research outputs found

    Embedding Four-directional Paths on Convex Point Sets

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    A directed path whose edges are assigned labels "up", "down", "right", or "left" is called \emph{four-directional}, and \emph{three-directional} if at most three out of the four labels are used. A \emph{direction-consistent embedding} of an \mbox{nn-vertex} four-directional path PP on a set SS of nn points in the plane is a straight-line drawing of PP where each vertex of PP is mapped to a distinct point of SS and every edge points to the direction specified by its label. We study planar direction-consistent embeddings of three- and four-directional paths and provide a complete picture of the problem for convex point sets.Comment: 11 pages, full conference version including all proof

    Hamiltonicity of 3-arc graphs

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    An arc of a graph is an oriented edge and a 3-arc is a 4-tuple (v,u,x,y)(v,u,x,y) of vertices such that both (v,u,x)(v,u,x) and (u,x,y)(u,x,y) are paths of length two. The 3-arc graph of a graph GG is defined to have vertices the arcs of GG such that two arcs uv,xyuv, xy are adjacent if and only if (v,u,x,y)(v,u,x,y) is a 3-arc of GG. In this paper we prove that any connected 3-arc graph is Hamiltonian, and all iterative 3-arc graphs of any connected graph of minimum degree at least three are Hamiltonian. As a consequence we obtain that if a vertex-transitive graph is isomorphic to the 3-arc graph of a connected arc-transitive graph of degree at least three, then it is Hamiltonian. This confirms the well known conjecture, that all vertex-transitive graphs with finitely many exceptions are Hamiltonian, for a large family of vertex-transitive graphs. We also prove that if a graph with at least four vertices is Hamilton-connected, then so are its iterative 3-arc graphs.Comment: in press Graphs and Combinatorics, 201

    Shift invariant preduals of &#8467;<sub>1</sub>(&#8484;)

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    The Banach space &#8467;&lt;sub&gt;1&lt;/sub&gt;(&#8484;) admits many non-isomorphic preduals, for example, C(K) for any compact countable space K, along with many more exotic Banach spaces. In this paper, we impose an extra condition: the predual must make the bilateral shift on &#8467;&lt;sub&gt;1&lt;/sub&gt;(&#8484;) weak&lt;sup&gt;*&lt;/sup&gt;-continuous. This is equivalent to making the natural convolution multiplication on &#8467;&lt;sub&gt;1&lt;/sub&gt;(&#8484;) separately weak*-continuous and so turning &#8467;&lt;sub&gt;1&lt;/sub&gt;(&#8484;) into a dual Banach algebra. We call such preduals &lt;i&gt;shift-invariant&lt;/i&gt;. It is known that the only shift-invariant predual arising from the standard duality between C&lt;sub&gt;0&lt;/sub&gt;(K) (for countable locally compact K) and &#8467;&lt;sub&gt;1&lt;/sub&gt;(&#8484;) is c&lt;sub&gt;0&lt;/sub&gt;(&#8484;). We provide an explicit construction of an uncountable family of distinct preduals which do make the bilateral shift weak&lt;sup&gt;*&lt;/sup&gt;-continuous. Using Szlenk index arguments, we show that merely as Banach spaces, these are all isomorphic to c&lt;sub&gt;0&lt;/sub&gt;. We then build some theory to study such preduals, showing that they arise from certain semigroup compactifications of &#8484;. This allows us to produce a large number of other examples, including non-isometric preduals, and preduals which are not Banach space isomorphic to c&lt;sub&gt;0&lt;/sub&gt;

    A comparison of variational and Markov chain Monte Carlo methods for inference in partially observed stochastic dynamic systems

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    In recent work we have developed a novel variational inference method for partially observed systems governed by stochastic differential equations. In this paper we provide a comparison of the Variational Gaussian Process Smoother with an exact solution computed using a Hybrid Monte Carlo approach to path sampling, applied to a stochastic double well potential model. It is demonstrated that the variational smoother provides us a very accurate estimate of mean path while conditional variance is slightly underestimated. We conclude with some remarks as to the advantages and disadvantages of the variational smoother. Ā© 2008 Springer Science + Business Media LLC
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