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Let I be a finite interval and r ∈ N. Denote by ∆ s + L q the subset of all functions y ∈ L q such that the s-difference ∆ s τ y(·) is nonnegative on I, ∀τ > 0. Further, denote by ∆ s + W r p , the class of functions x on I with the seminorm x (r) L p ≤ 1, such that ∆ s τ x ≥ 0, τ > 0. For s = 3,. .. , r + 1, we obtain two-sided estimates of the shape… (More)

- Shai Dekel, Dany Leviatan
- SIAM J. Math. Analysis
- 2004

The Bramble-Hilbert lemma is a fundamental result on multivariate polynomial approximation. It is frequently applied in the analysis of Finite Element Methods (FEM) used for numerical solutions of PDEs. However, this classical estimate depends on the geometry of the domain and may 'blow-up' for simple examples such as a sequence of triangles of equivalent… (More)

- Albert Cohen, Mark A. Davenport, Dany Leviatan
- Foundations of Computational Mathematics
- 2013

- Ronald A. DeVore, George Kyriazis, Dany Leviatan, Vladimir M. Tikhomirov
- Adv. Comput. Math.
- 1993

It is shown that certain algorithms of compression based on wavelet decompositions are optimal in the sense of nonlinear n-widths. 1. Introduction The use of wavelet decompositions for image and data compression has recently received much attention. The question arises as to whether wavelets have any significant advantage over other methods of compression… (More)

- D. Leviatan
- 1991

We are going to survey recent developments and achievements in shape preserving approximation by polynomials. We wish to approximate a function f deened on a nite interval, say ?1; 1], while preserving certain intrinsic \shape" properties. To be speciic we demand that the approximation process preserve properties of f , like its sign in all or part of the… (More)

Let I be a finite interval, r ∈ N and ρ(t) = dist{t, ∂I}, t ∈ I. Denote by ∆ s + L q the subset of all functions y ∈ L q such that the s-difference ∆ s τ y(t) is nonnegative on I, ∀τ > 0. Further, denote by ∆ s + W r p,α , 0 ≤ α < ∞ the classes of functions x on I with the seminorm x (r) ρ α L p ≤ 1, such that ∆ s τ x ≥ 0, τ > 0. For s = 0, 1, 2, we obtain… (More)

Let f 2 C ?1; 1] change its convexity nitely many times in the interval, say s times, at Y s : ?1 < y 1 < < y s < 1. We estimate the degree of approximation of f by polynomials of degree n, which change convexity exactly at the points Y s. We show that provided n is suuciently large, depending on the location of the points Y s , the rate of approximation is… (More)

- S. Dekel, D. Leviatan, M. Sharir
- 2003

In recent years there have been various attempts at the representations of multi-variate signals such as images, which outperform wavelets. As is well known wavelets are not optimal in that they do not take full advantage of the geometrical regularities and singularities of the images. Thus these approaches have been based on tracing curves of singularities… (More)

- Dany Leviatan, Vladimir N. Temlyakov
- J. Complexity
- 2005

We study nonlinear m-term approximation with regard to a redundant dictionary D in a Banach space. It is known that in the case of Hilbert space H the Pure Greedy Algorithm (or, more generally, the Weak Greedy Algorithm) provides for each f ∈ H and any dictionary D an expansion into a series f = ∞ j=1 c j (f)ϕ j (f), ϕ j (f) ∈ D, j = 1, 2,. .. with the… (More)

- R A Devore, D Leviatan, I A Shevchuk
- 1997

When we approximate a continuous nondecreasing function f in ?1; 1], we wish sometimes that also the approximating polynomi-als be nondecreasing. However, this constraint restricts very much the degree of approximation that the polynomials can achieve, namely, only the rate of ! 2 (f; 1=n). It turns out as we will prove somewhere else that relaxing the… (More)