The modified method of refined bounds for polyhedral approximation of convex polytopes

The modified method of refined bounds is proposed and experimentally studied. This method is designed to iteratively approximate convex multidimensional polytopes with a large number of vertices. Approximation is realized by a sequence of convex polytopes with a relatively small but gradually increasing number of vertices. The results of an experimental comparison between the modified and the original methods of refined bounds are presented. The latter was designed for the polyhedral approximation of multidimensional convex compact bodies of general type.     

Guaranteed-quality parallel Delaunay refinement for restricted polyhedral domains

We describe a distributed memory parallel Delaunay refinement algorithm for simple polyhedral domains whose constituent bounding edges and surfaces are separated by angles between 90° to 270° inclusive. With these constraints, our algorithm can generate meshes containing tetrahedra with circumradius to shortest edge ratio less than 2, and can tolerate more than 80% of the communication latency caused by unpredictable and variable remote gather operations.

Our experiments show that the algorithm is efficient in practice, even for certain domains whose boundaries do not conform to the theoretical limits imposed by the algorithm. The algorithm we describe is the first step in the development of much more sophisticated guaranteed-quality parallel mesh generation algorithms.     

TT-cross approximation for multidimensional arrays

As is well known, a rank-r matrix can be recovered from a cross of r linearly independent columns and rows, and an arbitrary matrix can be interpolated on the cross entries. Other entries by this cross or pseudo-skeleton approximation are given with errors depending on the closeness of the matrix to a rank-r matrix and as well on the choice of cross. In this paper we extend this construction to d-dimensional arrays (tensors) and suggest a new interpolation formula in which a d-dimensional array is interpolated on the entries of some TT-cross (tensor train-cross). The total number of entries and the complexity of our interpolation algorithm depend on d linearly, so the approach does not suffer from the curse of dimensionality.We also propose a TT-cross method for computation of d-dimensional integrals and apply it to some examples with dimensionality in the range from d=100 up to d=4000 and the relative accuracy of order 10^-10. In all constructions we capitalize on the new tensor decomposition in the form of tensor trains (TT-decomposition).     

Partition of unity refinement for local approximation

In this article, we propose a Partition of Unity Refinement (PUR) method to improve the local approximations of elliptic boundary value problems in regions of interest. The PUR method only needs to refine the local meshes and hanging nodes generate no difficulty. The mesh qualities such as uniformity or quasi‐uniformity are kept. The advantages of the PUR include its effectiveness and relatively easy implementation. In this article, we present the basic ideas and implementation of the PUR method on triangular meshes. Numerical results for elliptic Dirichlet boundary value problem on an L‐shaped domain are shown to demonstrate the effectiveness of the proposed method. The extensions of the PUR method to multilevel and higher dimension are straightforward. © 2010 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 27: 803–817, 2011     

Error bounds for multidimensional Laplace approximation

A numerical estimate is obtained for the error associated with the Laplace approximation of the double integral I(λ) = ∝∝_D g(x,y) e^−λf(x,y) dx dy, where D is a domain in , λ is a large positive parameter, f(x, y) and g(x, y) are real-valued and sufficiently smooth, and ∝(x, y) has an absolute minimum in D. The use of the estimate is illustrated by applying it to two realistic examples. The method used here applies also to higher dimensional integrals.     

An approximation method to estimate the Hausdorff measure of the Sierpinski gasket

In this paper, we firstly define a decreasing sequence {P^n(S)} by the generation of the Sierpinski gasket where each P^n(S) can be obtained in finite steps. Then we prove that the Hausdorff measure H^8(S) of the Sierpinski gasket S can be approximated by {P^n(S)} with P^n(S)/(1 1/2^n-3)s ≤ H^8(S)≤ Pb(S).An algorithm is presented to get P^n(S) for n≤ 5. As an application, we obtain the best lower bound of H^8(S) till now: H^8(S) ≥ 0.5631.     

Analytic and polyhedral approximation of convex bodies in separable polyhedral Banach spaces

A closed, convex and bounded setP in a Banach spaceE is called a polytope if every finite-dimensional section ofP is a polytope. A Banach spaceE is called polyhedral ifE has an equivalent norm such that its unit ball is a polytope. We prove here:

Smooth approximation of polyhedral surfaces regarding curvatures

In this paper we prove that every closed polyhedral surface in Euclidean three-space can be approximated (uniformly with respect to the Hausdorff metric) by smooth surfaces of the same topological type such that not only the (Gaussian) curvature but also the absolute curvature and the absolute mean curvature converge in the measure sense. This gives a direct connection between the concepts of total absolute curvature for both smooth and polyhedral surfaces which have been worked out by several authors, particularly N. H. Kuiper and T. F. Banchoff.The present paper is a detailed version of the short announcement [3].     

Properties of a method for polyhedral approximation of the feasible criterion set in convex multiobjective problems

The paper describes new results in the field of multiobjective optimization techniques. The Interactive Decision Maps (IDM) technique is based on approximation of Feasible Criterion Set (FCS) and subsequent visualization of the Pareto frontier of FCS by interactive displaying the bi-criteria slices of FCS. The Estimation Refinement (ER) method is now the main method for approximating convex FCS in the framework of IDM. The properties of the ER method are studied. We prove that the number of facets of the approximation constructed by ER and the number of the support function calculations of an approximated set are asymptotically optimal. These results are important from the point of view of real-life applications of ER.     

Mixed means and inequalities of Hardy and Levin-Cochran-Lee type for multidimensional balls

Integral means of arbitrary order, with power weights and their companion means, where the integrals are taken over balls in centered at the origin, are introduced and related mixed-means inequalities are derived. These relations are then used in obtaining Hardy and Levin-Cochran-Lee inequalities and their companion results for -dimensional balls. Finally, the best possible constants for these inequalities are obtained.

     

An error estimate for least squares approximation

Upper and lower error bounds are obtained for the error of the bestL ₂ polynomial approximation of degreen for a function belonging toC ⁿ⁺¹ [?1, 1].     

Spline approximation methods for multidimensional periodic pseudodifferential equations

We investigate several numerical methods for solving the pseudodifferential equationAu=f on the n-dimensional torusT ⁿ . We examine collocation methods as well as Galerkin-Petrov methods using various periodical spline functions. The considered spline spaces are subordinated to a uniform rectangular or triangular grid. For given approximation method and invertible pseudodifferential operatorA we compute a numerical symbol ^C , resp. ^G , depending onA and on the approximation method. It turns out that the stability of the numerical method is equivalent to the ellipticity of the corresponding numerical symbol. The case of variable symbols is tackled by a local principle. Optimal error estimates are established.The second author has been supported by a grant of Deutsche Forschungsgemeinschaft under grant namber Ko 634/32-1.     

Geometric applications of Chernoff-type estimates and a ZigZag approximation for balls

In this paper we show that the euclidean ball of radius in can be approximated up to , in the Hausdorff distance, by a set defined by linear inequalities. We call this set a ZigZag set, and it is defined to be all points in space satisfying or more of the inequalities. The constant we get is , where is some universal constant. This should be compared with the result of Barron and Cheang (2000), who obtained . The main ingredient in our proof is the use of Chernoff's inequality in a geometric context. After proving the theorem, we describe several other results which can be obtained using similar methods.

     

Error estimate for the approximation of nonlinear conservation laws on bounded domains by the finite volume method

In this paper we derive a priori and a posteriori error estimates for cell centered finite volume approximations of nonlinear conservation laws on polygonal bounded domains. Numerical experiments show the applicability of the a posteriori result for the derivation of local adaptive solution strategies.

     

Stability of spline approximation methods for multidimensional pseudodifferential operators

The present paper provides stability considerations of spline approximation methods for multidimensional singular operators. This paper should be regarded as a first step in establishing spline approximation methods for pseudodifferential operators on manifolds.     

A useful estimate in the multidimensional invariance principle

Summary An estimate of the convergence speed in the multidimensional invariance principle is obtained. Using this estimate, we can prove strong invariance principles for partial sums of independent not necessarily identically distributed multidimensional random vectors.     

The normal approximation rate for the drift estimator of multidimensional diffusions

We consider the problem of the density and drift estimation by the observation of a trajectory of an \mathbbR^d{\mathbb{R}^{d}}-dimensional homogeneous diffusion process with a unique invariant density. We construct estimators of the kernel type based on discretely sampled observations and study their asymptotic distribution. An estimate of the rate of normal approximation is given.