935 research outputs found
Appendix to "Approximating perpetuities"
An algorithm for perfect simulation from the unique solution of the
distributional fixed point equation is constructed, where
and are independent and is uniformly distributed on . This
distribution comes up as a limit distribution in the probabilistic analysis of
the Quickselect algorithm. Our simulation algorithm is based on coupling from
the past with a multigamma coupler. It has four lines of code
Long and short paths in uniform random recursive dags
In a uniform random recursive k-dag, there is a root, 0, and each node in
turn, from 1 to n, chooses k uniform random parents from among the nodes of
smaller index. If S_n is the shortest path distance from node n to the root,
then we determine the constant \sigma such that S_n/log(n) tends to \sigma in
probability as n tends to infinity. We also show that max_{1 \le i \le n}
S_i/log(n) tends to \sigma in probability.Comment: 16 page
Connectivity of sparse Bluetooth networks
Consider a random geometric graph defined on n vertices uniformly distributed in the d-dimensional unit torus. Two vertices are connected if their distance is less than a “visibility radius ” rn. We consider Bluetooth networks that are locally sparsified random geometric graphs. Each vertex selects c of its neighbors in the random geometric graph at random and connects only to the selected points. We show that if the visibility radius is at least of the order of n−(1−δ)/d for some δ> 0, then a constant value of c is sufficient for the graph to be connected, with high probability. It suffices to take c ≥ √ (1 + ɛ)/δ + K for any positive ɛ where K is a constant depending on d only. On the other hand, with c ≤ √ (1 − ɛ)/δ, the graph is disconnected, with high probability. 1 Introduction an
Simulating quantum correlations as a distributed sampling problem
It is known that quantum correlations exhibited by a maximally entangled
qubit pair can be simulated with the help of shared randomness, supplemented
with additional resources, such as communication, post-selection or non-local
boxes. For instance, in the case of projective measurements, it is possible to
solve this problem with protocols using one bit of communication or making one
use of a non-local box. We show that this problem reduces to a distributed
sampling problem. We give a new method to obtain samples from a biased
distribution, starting with shared random variables following a uniform
distribution, and use it to build distributed sampling protocols. This approach
allows us to derive, in a simpler and unified way, many existing protocols for
projective measurements, and extend them to positive operator value
measurements. Moreover, this approach naturally leads to a local hidden
variable model for Werner states.Comment: 13 pages, 2 figure
Simulation of bipartite qudit correlations
We present a protocol to simulate the quantum correlations of an arbitrary
bipartite state, when the parties perform a measurement according to two
traceless binary observables. We show that bits of classical
communication is enough on average, where is the dimension of both systems.
To obtain this result, we use the sampling approach for simulating the quantum
correlations. We discuss how to use this method in the case of qudits.Comment: 7 page
Devroye Inequality for a Class of Non-Uniformly Hyperbolic Dynamical Systems
In this paper, we prove an inequality, which we call "Devroye inequality",
for a large class of non-uniformly hyperbolic dynamical systems (M,f). This
class, introduced by L.-S. Young, includes families of piece-wise hyperbolic
maps (Lozi-like maps), scattering billiards (e.g., planar Lorentz gas),
unimodal and H{\'e}non-like maps. Devroye inequality provides an upper bound
for the variance of observables of the form K(x,f(x),...,f^{n-1}(x)), where K
is any separately Holder continuous function of n variables. In particular, we
can deal with observables which are not Birkhoff averages. We will show in
\cite{CCS} some applications of Devroye inequality to statistical properties of
this class of dynamical systems.Comment: Corrected version; To appear in Nonlinearit
Statistical properties of determinantal point processes in high-dimensional Euclidean spaces
The goal of this paper is to quantitatively describe some statistical
properties of higher-dimensional determinantal point processes with a primary
focus on the nearest-neighbor distribution functions. Toward this end, we
express these functions as determinants of matrices and then
extrapolate to . This formulation allows for a quick and accurate
numerical evaluation of these quantities for point processes in Euclidean
spaces of dimension . We also implement an algorithm due to Hough \emph{et.
al.} \cite{hough2006dpa} for generating configurations of determinantal point
processes in arbitrary Euclidean spaces, and we utilize this algorithm in
conjunction with the aforementioned numerical results to characterize the
statistical properties of what we call the Fermi-sphere point process for to 4. This homogeneous, isotropic determinantal point process, discussed
also in a companion paper \cite{ToScZa08}, is the high-dimensional
generalization of the distribution of eigenvalues on the unit circle of a
random matrix from the circular unitary ensemble (CUE). In addition to the
nearest-neighbor probability distribution, we are able to calculate Voronoi
cells and nearest-neighbor extrema statistics for the Fermi-sphere point
process and discuss these as the dimension is varied. The results in this
paper accompany and complement analytical properties of higher-dimensional
determinantal point processes developed in \cite{ToScZa08}.Comment: 42 pages, 17 figure
General lower bounds for evolutionary algorithms
Evolutionary optimization, among which genetic optimization, is a general framework for optimization. It is known (i) easy to use (ii) robust (iii) derivative-free (iv) unfortunately slow. Recent work [8] in particular show that the convergence rate of some widely used evolution strategies (evolutionary optimization for continuous domains) can not be faster than linear (i.e. the logarithm of the distance to the optimum can not decrease faster than linearly), and that the constant in the linear convergence (i.e. the constant C such that the distance to the optimum after n steps is upp er b ounded by C n ) unfortunately converges quickly to 1 as the dimension increases to infinity. We here show a very wide generalization of this result: al l comparison-based algorithms have such a limitation. Note that our result also concerns methods like the Hooke & Jeeves algorithm, the simplex method, or any direct search method that only compares the values to previously seen values of the fitness. But it does not cover methods that use the value of the fitness (see [5] for cases in which the fitness-values are used), even if these methods do not use gradients. The former results deal with convergence with respect to the number of comparisons performed, and also include a very wide family of algorithms with resp ect to the number of function-evaluations. However, there is still place for faster convergence rates, for more original algorithms using the full ranking information of the population and not only selections among the population. We prove that, at least in some particular cases, using the full ranking information can improve these lower bounds, and ultimately provide sup erlinear convergence results
Integration of Langevin Equations with Multiplicative Noise and Viability of Field Theories for Absorbing Phase Transitions
Efficient and accurate integration of stochastic (partial) differential
equations with multiplicative noise can be obtained through a split-step
scheme, which separates the integration of the deterministic part from that of
the stochastic part, the latter being performed by sampling exactly the
solution of the associated Fokker-Planck equation. We demonstrate the
computational power of this method by applying it to most absorbing phase
transitions for which Langevin equations have been proposed. This provides
precise estimates of the associated scaling exponents, clarifying the
classification of these nonequilibrium problems, and confirms or refutes some
existing theories.Comment: 4 pages. 4 figures. RevTex. Slightly changed versio
Massively Parallel Construction of Radix Tree Forests for the Efficient Sampling of Discrete Probability Distributions
We compare different methods for sampling from discrete probability
distributions and introduce a new algorithm which is especially efficient on
massively parallel processors, such as GPUs. The scheme preserves the
distribution properties of the input sequence, exposes constant time complexity
on the average, and significantly lowers the average number of operations for
certain distributions when sampling is performed in a parallel algorithm that
requires synchronization afterwards. Avoiding load balancing issues of na\"ive
approaches, a very efficient massively parallel construction algorithm for the
required auxiliary data structure is complemented
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