Gaussian beam decomposition of high frequency wave fields

In this paper, we present a method of decomposing a highly oscillatory wave field into a sparse superposition of Gaussian beams. The goal is to extract the necessary parameters for a Gaussian beam superposition from this wave field, so that further evolution of the high frequency waves can be computed by the method of Gaussian beams. The methodology is described for R^d${\mathbb{R}}^d$ with numerical examples for d=2$d=2$. In the first example, a field generated by an interface reflection of Gaussian beams is decomposed into a superposition of Gaussian beams. The beam parameters are reconstructed to a very high accuracy. The data in the second example is not a superposition of a finite number of Gaussian beams. The wave field to be approximated is generated by a finite difference method for a geometry with two slits. The accuracy in the decomposition increases monotonically with the number of beams.     

Mixtures of truncated basis functions

In this paper we propose a framework, called mixtures of truncated basis functions (MoTBFs), for representing general hybrid Bayesian networks. The proposed framework generalizes both the mixture of truncated exponentials (MTEs) framework and the Mixture of Polynomials (MoPs) framework. Similar to MTEs and MoPs, MoTBFs are defined so that the potentials are closed under combination and marginalization, which ensures that inference in MoTBF networks can be performed efficiently using the Shafer-Shenoy architecture.Based on a generalized Fourier series approximation, we devise a method for efficiently approximating an arbitrary density function using the MoTBF framework. The translation method is more flexible than existing MTE or MoP-based methods, and it supports an online/anytime tradeoff between the accuracy and the complexity of the approximation. Experimental results show that the approximations obtained are either comparable or significantly better than the approximations obtained using existing methods.     

A Discrete Chebyshev Approximation Problem by Means of Several Polynomials

The problem of approximation of a given function on a given set by a polynomial of a fixed degree in the Chebyshev metric (the Chebyshev polynomial approximation problem) is a typical problem of Nonsmooth Analysis (to be more precise, it is a convex nonsmooth problem). It has many important applications, both in mathematics and in practice. The theory of Chebyshev approximations enjoys very nice properties (the most famous being the Chebyshev alternation rule). In the present article the problem of approximation of a given function on a given finite set of points by several polynomials is studied. As a criterion function, the maximin deviation is taken. The resulting functional is nonsmooth and nonconvex and therefore the problem becomes multiextremal and may have local minimizers which are not global ones. A necessary and sufficient condition for a point to be a local minimizer is proved. It is shown that a generalized alternation rule is still valid. Sufficient conditions for a point to be a strict local minimizer are established as well. These conditions are also formulated in terms of alternants. An exchange algorithm for finding a local minimizer is constructed. An k -exchange algorithm, allowing to find a "better" local minimizer is stated. Numerical examples illustrating the theory are presented.     

A numerical evaluation of some collinear scaling algorithms for unconstrained

Collinear scaling algorithms for unconstrained minimization were first proposed by Davidon (1977,80) so that they may incorporate more information about the problem than is possible with quasi–Newton algorithms. Sorensen (1980,82), and Ariyawansa (1983,90) have derived collinear scaling algorithms as natural extensions of quasi–Newton algorithms. In this paper we describe the results of a comprehensive numerical evaluation of four members in the classes of collinear scaling algorithms derived by Sorensen (1980,82) and Ariyawansa (1983,90), relative to the quasi–Newton algorithms they extend.     

Ramanujan formula for the generalized Stirling approximation

The aim of this paper is to improve Ramanujan’s formula for approximation of the factorial function, starting from Burnside’s formula in contradistinction with the classical formula that starts from Stirling’s formula.     

Differential graded versus simplicial categories

We establish a connection between differential graded and simplicial categories by constructing a three-step zig-zag of Quillen adjunctions relating the homotopy theories of the two. In an intermediate step, we extend the Dold-Kan correspondence to a Quillen equivalence between categories enriched over non-negatively graded complexes and categories enriched over simplicial modules. As an application, we obtain a simple calculation of Simpson's homotopy fiber, which is known to be a key step in the construction of a moduli stack of perfect complexes on a smooth projective variety.