Given a hyperbolic surface with geodesic boundary S, the lengths of a maximal system of disjoint simple geodesic arcs on S that start and end at @S perpendicularly are coordinates on the Teichm¨uller space T (S). We express the Weil-Petersson Poisson structure of T (S) in this system of coordinates, and we prove that it limits pointwise to the piecewise-linear Poisson structure defined by Kontsevich on the arc complex of S. At the same time, we obtain a formula for the first-order variation of the distance between two closed geodesics under Fenchel-Nielsen deformation.

Let$H$^{∞}be the algebra of bounded analytic functions in the unit disk$D$. Let$I=I(f$_{1},...,$f$_{N}) be the ideal generated by$f$_{1},...,$f$_{N}∈$H$^{∞}and$J=J(f$_{1},...,$f$_{N}) the ideal of the functions$f∈H$^{∞}for which there exists a constant$C=C(f)$such that |$f(z)|≤C(|f$_{1}$(z)|+$...;$+|f$_{N}$(z)$|),$z$∈$D$. It is clear that $$I \subseteq J$$ , but an example due to J. Bourgain shows that$J$is not, in general, in the norm closure of$I$. Our first result asserts that$J$is included in the norm closure of$I$if$I$contains a Carleson-Newman Blaschke product, or equivalently, if there exists$s$>0 such that $$\mathop {\inf }\limits_{z \in D} \sum\limits_{k = 0}^s {(1 - |z|)^k } \sum\limits_{j = 1}^N {|f_j^{(k)} (z)| > 0.} $$

We compute certain open Gromov-Witten invariants for toric Calabi-Yau threefolds. The proof relies on a relation for ordinary Gromov-Witten invariants for threefolds under certain birational transformation, and a recent result of Kwokwai Chan.

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Block coordinate update (BCU) methods enjoy low per-update computational complexity because every time only one or a few block variables would need to be updated among possibly a large number of blocks. They are also easily parallelized and thus have been particularly popular for solving problems involving large-scale dataset and/or variables. In this paper, we propose a primaldual BCU method for solving linearly constrained convex program with multi-block variables. The method is an accelerated version of a primaldual algorithm proposed by the authors, which applies randomization in selecting block variables to update and establishes an <i>O</i>(1/<i>t</i>) convergence rate under convexity assumption. We show that the rate can be accelerated to O ( 1 / t 2 ) if the objective is strongly convex. In addition, if one block variable is independent of the others in the objective, we then show that the algorithm can