English

Markov $L_2$ inequality with the Gegenbauer weight

Classical Analysis and ODEs 2017-02-21 v1

Abstract

For the Gegenbauer weight function wλ(t)=(1t2)λ1/2w_{\lambda}(t)=(1-t^2)^{\lambda-1/2}, λ>1/2\lambda>-1/2, we denote by wλ\Vert\cdot\Vert_{w_{\lambda}} the associated L2L_2-norm, fwλ:=(11wλ(t)f2(t)dt)1/2. \Vert f\Vert_{w_{\lambda}}:=\Big(\int_{-1}^{1}w_{\lambda}(t)f^2(t)\,dt\Big)^{1/2}. We study the Markov inequality pwλcn(λ)pwλ,pPn, \Vert p^{\prime}\Vert_{w_{\lambda}}\leq c_{n}(\lambda)\,\Vert p\Vert_{w_{\lambda}},\qquad p\in \mathcal{P}_n, where Pn\mathcal{P}_n is the class of algebraic polynomials of degree not exceeding nn. Upper and lower bounds for the best Markov constant cn(λ)c_{n}(\lambda) are obtained, which are valid for all nNn\in \mathbb{N} and λ>12\lambda>-\frac{1}{2}.

Keywords

Cite

@article{arxiv.1702.05963,
  title  = {Markov $L_2$ inequality with the Gegenbauer weight},
  author = {Dragomir Aleksov and Geno Nikolov},
  journal= {arXiv preprint arXiv:1702.05963},
  year   = {2017}
}

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14 pages