3,571 research outputs found
Permutation graphs, fast forward permutations, and sampling the cycle structure of a permutation
A permutation P on {1,..,N} is a_fast_forward_permutation_ if for each m the
computational complexity of evaluating P^m(x)$ is small independently of m and
x. Naor and Reingold constructed fast forward pseudorandom cycluses and
involutions. By studying the evolution of permutation graphs, we prove that the
number of queries needed to distinguish a random cyclus from a random
permutation on {1,..,N} is Theta(N) if one does not use queries of the form
P^m(x), but is only Theta(1) if one is allowed to make such queries.
We construct fast forward permutations which are indistinguishable from
random permutations even when queries of the form P^m(x) are allowed. This is
done by introducing an efficient method to sample the cycle structure of a
random permutation, which in turn solves an open problem of Naor and Reingold.Comment: Corrected a small erro
Optimizing Low-Discrepancy Sequences with an Evolutionary Algorithm
International audienceMany elds rely on some stochastic sampling of a given com- plex space. Low-discrepancy sequences are methods aim- ing at producing samples with better space-lling properties than uniformly distributed random numbers, hence allow- ing a more ecient sampling of that space. State-of-the-art methods like nearly orthogonal Latin hypercubes and scram- bled Halton sequences are congured by permutations of in- ternal parameters, where permutations are commonly done randomly. This paper proposes the use of evolutionary al- gorithms to evolve these permutations, in order to optimize a discrepancy measure. Results show that an evolution- ary method is able to generate low-discrepancy sequences of signicantly better space-lling properties compared to sequences congured with purely random permutations
A sparse decomposition of low rank symmetric positive semi-definite matrices
Suppose that is symmetric positive
semidefinite with rank . Our goal is to decompose into
rank-one matrices where the modes
are required to be as sparse as possible. In contrast to eigen decomposition,
these sparse modes are not required to be orthogonal. Such a problem arises in
random field parametrization where is the covariance function and is
intractable to solve in general. In this paper, we partition the indices from 1
to into several patches and propose to quantify the sparseness of a vector
by the number of patches on which it is nonzero, which is called patch-wise
sparseness. Our aim is to find the decomposition which minimizes the total
patch-wise sparseness of the decomposed modes. We propose a
domain-decomposition type method, called intrinsic sparse mode decomposition
(ISMD), which follows the "local-modes-construction + patching-up" procedure.
The key step in the ISMD is to construct local pieces of the intrinsic sparse
modes by a joint diagonalization problem. Thereafter a pivoted Cholesky
decomposition is utilized to glue these local pieces together. Optimal sparse
decomposition, consistency with different domain decomposition and robustness
to small perturbation are proved under the so called regular-sparse assumption
(see Definition 1.2). We provide simulation results to show the efficiency and
robustness of the ISMD. We also compare the ISMD to other existing methods,
e.g., eigen decomposition, pivoted Cholesky decomposition and convex relaxation
of sparse principal component analysis [25] and [40]
Generating Random Elements of Finite Distributive Lattices
This survey article describes a method for choosing uniformly at random from
any finite set whose objects can be viewed as constituting a distributive
lattice. The method is based on ideas of the author and David Wilson for using
``coupling from the past'' to remove initialization bias from Monte Carlo
randomization. The article describes several applications to specific kinds of
combinatorial objects such as tilings, constrained lattice paths, and
alternating-sign matrices.Comment: 13 page
- …