Asymptotics of Some Plancherel Averages via Polynomiality Results

Consider Young diagrams of $n$ boxes distributed according to the Plancherel measure. So those diagrams could be the output of the RSK algorithm, when applied to random permutations of the set $\{1,\ldots,n\}$. Here we are interested in asymptotics, as $n\to \infty$, of expectations of certain functions of random Young diagrams, such as the number of bumping steps of the RSK algorithm that leads to that diagram, the side length of its Durfee square, or the logarithm of its probability. We can express these functions in terms of hook lengths or contents of the boxes of the diagram, which opens the door for application of known polynomiality results for Plancherel averages. We thus obtain representations of expectations as binomial convolutions, that can be further analyzed with the help of Rice's integral or Poisson generating functions. Among our results is a very explicit expression for the constant appearing in the almost equipartition property of the Plancherel measure

On the variance of a class of inductive valuations of data structures for digital search

AbstractLet an inductive valuation L on the family of binary tries or Patricia tries or digital search trees be defined in the following way: L(t) = L(tl) + L(tr) + R(t), where tl and tr denote the left and right subtrees of t and R depends only on the size (the number of records) ¦t¦ of t. Let LN denote L restricted to the trees of size N. In Theorem 1 we give sufficient conditions on the sequence r¦t¦ $̈= R(t) for the variance Var LN to be of exact order N, if the family of tries (resp. Patricia tries, resp. digital search trees) is equipped with the Bernoulli model. For the symmetric Bernoulli model we prove the existence of a continuous periodic function δ with period 1, such that Var LN ∼ δ(log2 N) .̄ N holds

Hessian barrier algorithms for linearly constrained optimization problems

In this paper, we propose an interior-point method for linearly constrained optimization problems (possibly nonconvex). The method - which we call the Hessian barrier algorithm (HBA) - combines a forward Euler discretization of Hessian Riemannian gradient flows with an Armijo backtracking step-size policy. In this way, HBA can be seen as an alternative to mirror descent (MD), and contains as special cases the affine scaling algorithm, regularized Newton processes, and several other iterative solution methods. Our main result is that, modulo a non-degeneracy condition, the algorithm converges to the problem's set of critical points; hence, in the convex case, the algorithm converges globally to the problem's minimum set. In the case of linearly constrained quadratic programs (not necessarily convex), we also show that the method's convergence rate is $\mathcal{O}(1/k^\rho)$ for some $\rho\in(0,1]$ that depends only on the choice of kernel function (i.e., not on the problem's primitives). These theoretical results are validated by numerical experiments in standard non-convex test functions and large-scale traffic assignment problems.Comment: 27 pages, 6 figure

Hessian barrier algorithms for linearly constrained optimization problems

International audienceIn this paper, we propose an interior-point method for linearly constrained-and possibly nonconvex-optimization problems. The method-which we call the Hessian barrier algorithm (HBA)-combines a forward Euler discretization of Hessian-Riemannian gradient flows with an Armijo backtracking step-size policy. In this way, HBA can be seen as an alternative to mirror descent, and contains as special cases the affine scaling algorithm, regularized Newton processes, and several other iterative solution methods. Our main result is that, modulo a nondegeneracy condition, the algorithm converges to the problem's critical set; hence, in the convex case, the algorithm converges globally to the problem's minimum set. In the case of linearly constrained quadratic programs (not necessarily convex), we also show that the method's convergence rate is $O(1/k^\rho)$ for some $\rho \in (0, 1]$ that depends only on the choice of kernel function (i.e., not on the problem's primi-tives). These theoretical results are validated by numerical experiments in standard nonconvex test functions and large-scale traffic assignment problems

An Innovative Integration Methodology of Independent Data Sources to Improve the Quality of Freight Transport Surveys

AbstractPast experiences show that data of the official Austrian freight transport statistics are often underestimated. Therefore, a methodology was developed, merging existing independent road freight transport data to a consistent and valid road freight matrix. The methodology comprises four steps, using data of the Austrian and European freight transport statistics, data of roadside interviews of truck drivers, and data of counting stations and toll gantries. The methodology was applied to data from the year 2009. Results show the reliability and plausibility of the methodology, indicated by a high correlation with high quality roadside traffic counts

Does moral play equilibrate?

Some finite and symmetric two-player games have no (pure or mixed) symmetric Nash equilibrium when played by partly morally motivated players. The reason is that the "right thing to do" may be not to randomize. We analyze this issue both under complete information between equally moral players and under incomplete information between arbitrarily moral players. We provide necessary and sufficient conditions for the existence of equilibrium and illustrate the results with examples and counter-examples

Does moral play equilibrate?

Some finite and symmetric two-player games have no (pure or mixed) symmetric Nash equilibrium when played by partly morally motivated players. The reason is that the "right thing to do" may be not to randomize. We analyze this issue both under complete information between equally moral players and under incomplete information between arbitrarily moral players. We provide necessary and sufficient conditions for the existence of equilibrium and illustrate the results with examples and counter-examples