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A quasi-Newton method with cubic regularization is designed for solving Riemannian unconstrained nonconvex optimization problems. The proposed algorithm is fully adaptive with at most ${\cal O} (\epsilon_g^{-3/2})$ iterations to achieve a…

Optimization and Control · Mathematics 2024-02-21 Mauricio S. Louzeiro , Gilson N. Silva , Jinyun Yuan , Daoping Zhang

Let $ f^{\star} $ be a function on $ \mathbb{R}^d $ with an assumption of a spectral norm $ v_{f^{\star}} $. For various noise settings, we show that $ \mathbb{E}\|\hat{f} - f^{\star} \|^2 \leq \left(v^4_{f^{\star}}\frac{\log…

Statistics Theory · Mathematics 2018-10-31 Jason M. Klusowski , Andrew R. Barron

In this paper we consider the so-called crystallographic Radon transform (or crystallographic $X$-ray transform) and totally geodesic Radon transform on the group of rotations SO(3). As we show both of these transforms naturally appear in…

Functional Analysis · Mathematics 2014-03-04 Swanhild Bernstein , Isaac Z. Pesenson

We propose a new solver for the sparse spikes deconvolution problem over the space of Radon measures. A common approach to off-the-grid deconvolution considers semidefinite (SDP) relaxations of the total variation (the total mass of the…

Optimization and Control · Mathematics 2019-03-12 Paul Catala , Vincent Duval , Gabriel Peyré

We study the H^{-1}-norm of the function 1 on tubular neighbourhoods of curves in R^2. We take the limit of small thickness epsilon, and we prove two different asymptotic results. The first is an asymptotic development for a fixed curve in…

Analysis of PDEs · Mathematics 2019-07-11 Yves van Gennip , Mark A. Peletier

Escaping saddle points is a central research topic in nonconvex optimization. In this paper, we propose a simple gradient-based algorithm such that for a smooth function $f\colon\mathbb{R}^n\to\mathbb{R}$, it outputs an…

Optimization and Control · Mathematics 2021-11-30 Chenyi Zhang , Tongyang Li

In this paper we explore the connection between quantitative rectifiability of measures and the $L^2$ boundedness of the codimension one Riesz transform. Among other things, we prove the following. Let $\mu$ be a Radon measure in $\mathbb…

Classical Analysis and ODEs · Mathematics 2026-02-10 Xavier Tolsa

In this paper we refer to the reconstruction formulas given in L.-E. Andersson's On the determination of a function from spherical averages, which are often used in applications such as SAR and SONAR. We demonstrate that the first one of…

Classical Analysis and ODEs · Mathematics 2007-05-23 Jens Klein

Several novel imaging applications have lead recently to a variety of Radon type transforms, where integration is done over a family of conical surfaces. We call them \emph{cone transforms} (in 2D they are also called \emph{V-line} or…

Functional Analysis · Mathematics 2015-09-24 Fatma Terzioglu

In this article we study the spherical mean Radon transform in $\mathbf R^3$ with detectors centered on a plane. We use the consistency method suggested by the author of this article for the inversion of the transform in 3D. A new iterative…

Classical Analysis and ODEs · Mathematics 2022-06-24 Rafik Aramyan

We provide a new algorithm for the treatment of the noisy inversion of the Radon transform using an appropriate thresholding technique adapted to a well-chosen new localized basis. We establish minimax results and prove their optimality. In…

Statistics Theory · Mathematics 2012-05-11 Gerard Kerkyacharian , Erwan Le Pennec , Dominique Picard

We investigate the sample complexity of networks with bounds on the magnitude of its weights. In particular, we consider the class \[ H=\left\{W_t\circ\rho\circ \ldots\circ\rho\circ W_{1} :W_1,\ldots,W_{t-1}\in M_{d, d}, W_t\in…

Machine Learning · Computer Science 2019-10-15 Amit Daniely , Elad Granot

In this paper we study the problem of minimizing the average of a large number ($n$) of smooth convex loss functions. We propose a new method, S2GD (Semi-Stochastic Gradient Descent), which runs for one or several epochs in each of which a…

Machine Learning · Statistics 2015-06-17 Jakub Konečný , Peter Richtárik

We study stochastic gradient descent {\em without replacement} (\sgdwor) for smooth convex functions. \sgdwor is widely observed to converge faster than true \sgd where each sample is drawn independently {\em with replacement}…

Optimization and Control · Mathematics 2020-02-28 Prateek Jain , Dheeraj Nagaraj , Praneeth Netrapalli

We extend the theorems of [G1] on $L^p$ to $L^p_s$ Sobolev improvement for translation invariant Radon and fractional singular Radon transforms over hypersurfaces, proving $L^p$ to $L^q_s$ boundedness results for such operators. Here $q…

Classical Analysis and ODEs · Mathematics 2019-10-11 Michael Greenblatt

In this short note, we consider self-similar immersions $F: \mathbb{R}^n \to \mathbb{R}^{n+k}$ of the Graphic Mean Curvature Flow of higher co-dimension. We show that the following is true: Let $F(x) = (x,f(x)), x \in \mathbb{R}^{n}$ be a…

Differential Geometry · Mathematics 2007-05-23 L. Ma , Y. Yang

The hyperbolic Radon transform is a commonly used tool in seismic processing, for instance in seismic velocity analysis, data interpolation and for multiple removal. A direct implementation by summation of traces with different moveouts is…

Numerical Analysis · Mathematics 2017-05-24 Viktor V. Nikitin , Fredrik Andersson , Marcus Carlsson , Anton A. Duchkov

In [J. Bures, R. Lavicka, V. Soucek, Elements of quaternionic analysis and Radon transform, Textos de Matematica 42, Departamento de Matematica, Universidade de Coimbra, 2009], the authors describe a link between holomorphic functions…

Complex Variables · Mathematics 2014-06-20 Fabrizio Colombo , Roman Lavicka , Irene Sabadini , Vladimir Soucek

We establish new upper and lower bounds on the number of queries required to test convexity of functions over various discrete domains. 1. We provide a simplified version of the non-adaptive convexity tester on the line. We re-prove the…

Computational Complexity · Computer Science 2019-08-08 Aleksandrs Belovs , Eric Blais , Abhinav Bommireddi

It is known that the Funk transform (the Funk-Radon transform) is invertible in the class of even (symmetric) continuous functions defined on the unit 2-sphere S^2. In this article, for the reconstruction of f from C(S^2) (can be non-even),…

Classical Analysis and ODEs · Mathematics 2024-11-11 Rafik Aramyan