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In this paper we study the sampling recovery problem for certain relevant multivariate function classes which are not compactly embedded into $L_\infty$. Recent tools relating the sampling numbers to the Kolmogorov widths in the uniform…

数值分析 · 数学 2022-10-05 Glenn Byrenheid , Serhii A. Stasyuk , Tino Ullrich

In this article, we present a greedy algorithm based on a tensor product decomposition, whose aim is to compute the global minimum of a strongly convex energy functional. We prove the convergence of our method provided that the gradient of…

泛函分析 · 数学 2015-03-13 Eric Cances , Virginie Ehrlacher , Tony Lelievre

Greedy algorithms have been successfully analyzed and applied in training neural networks for solving variational problems, ensuring guaranteed convergence orders. In this paper, we extend the analysis of the orthogonal greedy algorithm…

数值分析 · 数学 2025-04-21 Jinchao Xu , Xiaofeng Xu

In this paper we obtain quantitative weighted $L^p$-inequalities for some operators involving Bessel convolutions. We consider maximal operators, Littlewood-Paley functions and variational operators. We obtain $L^p(w)$-operator norms in…

经典分析与常微分方程 · 数学 2021-10-06 Víctor Almeida , Jorge J. Betancor , Juan C. Fariña , Lourdes Rodríguez-Mesa

Let $D\subset\mathbb C^n$ be a bounded, strongly pseudoconvex domain whose boundary $bD$ satisfies the minimal regularity condition of class $C^2$. A 2017 result of Lanzani \& Stein states that the Cauchy--Szeg\"{o} projection $S_\omega$…

复变函数 · 数学 2025-04-25 Xuan Thinh Duong , Loredana Lanzani , Ji Li , Brett D. Wick

We propose a class of greedy algorithms for weighted sparse recovery by considering new loss function-based generalizations of Orthogonal Matching Pursuit (OMP). Given a (regularized) loss function, the proposed algorithms alternate the…

信息论 · 计算机科学 2025-02-18 Sina Mohammad-Taheri , Simone Brugiapaglia

The general problem addressed in this work is the development of a systematic study of the thresholding greedy algorithm for general biorthogonal systems (also known as Markushevich bases) in quasi-Banach spaces from a functional-analytic…

泛函分析 · 数学 2019-03-29 Fernando Albiac , Jose L. Ansorena , Pablo M. Berna , Przemyslaw Wojtaszczyk

Many image processing applications benefited remarkably from the theory of sparsity. One model of sparsity is the cosparse analysis one. It was shown that using l_1-minimization one might stably recover a cosparse signal from a small set of…

数值分析 · 数学 2015-02-10 Raja Giryes

In [25], T. Oikhberg introduced and studied variants of the greedy and weak greedy algorithms for sequences with gaps, with a focus on the $\mathbf n$-$t$-quasi-greedy property that is based on them. Building upon this foundation, our…

泛函分析 · 数学 2023-09-04 Miguel Berasategui , Pablo M. Berná

We investigate various aspects of the "weighted" greedy algorithm with respect to a Schauder basis. For a weight w, we describe w-greedy, w-almost-greedy and w-partially-greedy bases, and show some properties of w-semi-greedy bases. To…

泛函分析 · 数学 2018-06-19 P. M. Berná , S. J. Dilworth , D. Kutzarova , T. Oikhberg , B. Wallis

We consider the $X$-Greedy Algorithm and the Dual Greedy Algorithm in a finite-dimensional Banach space with a strictly monotone basis as the dictionary. We show that when the dictionary is an initial segment of the Haar basis in $L_p[0,1]$…

泛函分析 · 数学 2015-05-13 S. J. Dilworth , E. Odell , Th. Schlumprecht , A. Zsak

By means of appropriate sparse bounds, we deduce compactness on weighted $L^p(w)$ spaces, $1<p<\infty$, for all Calder\'on-Zygmund operators having compact extensions on $L^2(\mathbb{R}^n)$. Similar methods lead to new results on…

经典分析与常微分方程 · 数学 2024-07-23 Cody B. Stockdale , Paco Villarroya , Brett D. Wick

We give weighted norm inequalities for the maximal fractional operator $ \mathcal M_{q,\beta}$ of Hardy-Littlewood and the fractional integral $I_{\gamma}$. These inequalities are established between $(L^{q},L^{p}) ^{\alpha}(X,d,\mu)$…

经典分析与常微分方程 · 数学 2009-01-28 Justin Feuto , Ibrahim Fofana , Konin Koua

We analyze greedy algorithms for the Hierarchical Aggregation (HAG) problem, a strategy introduced in [Jia et al., KDD 2020] for speeding up learning on Graph Neural Networks (GNNs). The idea of HAG is to identify and remove redundancies in…

数据结构与算法 · 计算机科学 2021-02-09 Alexandra Porter , Mary Wootters

We show that a very simple modification of the Pure Greedy Algorithm for approximating functions by sparse sums from a dictionary in a Hilbert or more generally a Banach space has optimal convergence rates on the class of convex…

数值分析 · 数学 2015-05-15 Guergana Petrova

We show weighted non-autonomous $L^q(L^p)$ maximal regularity for families of complex second-order systems in divergence form under a mixed regularity condition in space and time. To be more precise, we let $p,q \in (1,\infty)$ and we…

偏微分方程分析 · 数学 2025-07-15 Sebastian Bechtel

We consider structure learning of linear Gaussian structural equation models with weak edges. Since the presence of weak edges can lead to a loss of edge orientations in the true underlying CPDAG, we define a new graphical object that can…

统计方法学 · 统计学 2017-07-25 Marco F. Eigenmann , Preetam Nandy , Marloes H. Maathuis

We prove optimal integrability results for solutions of the $p(\cdot)$-Laplace equation in the scale of (weak) Lebesgue spaces. To obtain this, we show that variable exponent Riesz and Wolff potentials maps $L^1$ to variable exponent weak…

泛函分析 · 数学 2013-10-24 Alexandre Almeida , Petteri Harjulehto , Peter Hästö , Teemu Lukkari

We consider the problem of approximating a given element $f$ from a Hilbert space $\mathcal{H}$ by means of greedy algorithms and the application of such procedures to the regression problem in statistical learning theory. We improve on the…

统计理论 · 数学 2009-09-29 Andrew R. Barron , Albert Cohen , Wolfgang Dahmen , Ronald A. DeVore

We prove an inequality for the entropy numbers in terms of nonlinear Kolmogorov's widths. This inequality is in a spirit of known inequalities of this type and it is adjusted to the form convenient in applications for $m$-term…

度量几何 · 数学 2013-02-01 Vladimir Temlyakov