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This work addresses the robust reconstruction problem of a sparse signal from compressed measurements. We propose a robust formulation for sparse reconstruction which employs the $\ell_1$-norm as the loss function for the residual error and…

Information Theory · Computer Science 2017-03-30 Fei Wen , Yuan Yang , Ling Pei , Wenxian Yu , Peilin Liu

In the present paper we consider application of overcomplete dictionaries to solution of general ill-posed linear inverse problems. Construction of an adaptive optimal solution for such problems usually relies either on a singular value…

Methodology · Statistics 2015-04-03 Marianna Pensky

Regularisation theory in Banach spaces, and non--norm-squared regularisation even in finite dimensions, generally relies upon Bregman divergences to replace norm convergence. This is comparable to the extension of first-order optimisation…

Optimization and Control · Mathematics 2021-03-19 Tuomo Valkonen

These notes are devoted to the notion of well-posedness of the Cauchy problem for nonlinear dispersive equations. We present recent methods for proving ill-posedness type results for dispersive PDE's. The common feature in the analysis is…

Analysis of PDEs · Mathematics 2007-05-23 N. Tzvetkov

In this article we show the crucial role of elliptic regularity theory for the development of efficient numerical methods for the solution of some variational problems. Here we focus to a class of elliptic multiobjective optimal control…

Optimization and Control · Mathematics 2021-01-27 A. Dreves , J. Gwinner , N. Ovcharova

This article is devoted to forward and inverse problems associated with time-independent semilinear nonlocal wave equations. We first establish comprehensive well-posedness results for some semilinear nonlocal wave equations. The main…

Analysis of PDEs · Mathematics 2024-02-09 Yi-Hsuan Lin , Teemu Tyni , Philipp Zimmermann

We show that perturbing ill-posed differential equations with (potentially very) smooth random processes can restore well-posedness -- even if the perturbation is (potentially much) more regular than the drift component of the solution. The…

Probability · Mathematics 2024-09-25 Máté Gerencsér

This paper investigates the optimality conditions for characterizing the local minimizers of the constrained optimization problems involving an $\ell_p$ norm ($0<p<1$) of the variables, which may appear in either the objective or the…

Optimization and Control · Mathematics 2022-02-16 Hao Wang , Yining Gao , Jiashan Wang , Hongying Liu

This paper is concerned with the Cauchy problem of the quadratic nonlinear Schr\"{o}dinger equation in $\mathbb{R} \times \mathbb{R}^2$ with the nonlinearity $\eta |u|^2$ where $\eta \in \mathbb{C} \setminus \{0\}$ and low regularity…

Analysis of PDEs · Mathematics 2022-09-27 Hiroyuki Hirayama , Shinya Kinoshita , Mamoru Okamoto

Motivated by re-weighted $\ell_1$ approaches for sparse recovery, we propose a lifted $\ell_1$ (LL1) regularization which is a generalized form of several popular regularizations in the literature. By exploring such connections, we discover…

Signal Processing · Electrical Eng. & Systems 2022-05-13 Yaghoub Rahimi , Sung Ha Kang , Yifei Lou

For operators representing ill-posed problems, an ordering by ill-posedness is proposed, where one operator is considered more ill-posed than another one if the former can be expressed as a cocatenation of bounded operators involving the…

Functional Analysis · Mathematics 2025-02-06 Stefan Kindermann , Bernd Hofmann

In this article, we initiate the study of the Cauchy problem for the two-dimensional relativistic Euler equations in a low-regularity setting. By introducing good variables--a rescaled velocity, logarithmic enthalpy, and an appropriately…

Analysis of PDEs · Mathematics 2025-12-19 Huali Zhang

The recovery of sparse data is at the core of many applications in machine learning and signal processing. While such problems can be tackled using $\ell_1$-regularization as in the LASSO estimator and in the Basis Pursuit approach,…

Optimization and Control · Mathematics 2021-11-15 Christian Kümmerle , Claudio Mayrink Verdun , Dominik Stöger

We consider fractional wave equations with exponential or arbitrary polynomial nonlinearities. We prove the global well-posedness on the support of the corresponding Gibbs measures. We provide ill-posedness constructions showing that the…

Analysis of PDEs · Mathematics 2019-09-24 Chenmin Sun , Nikolay Tzvetkov

In this work we consider, in a Banach space framework, the regularization of linear ill-posed problems. Our focus is on the recovery of solutions that have a logarithmic source representation. Such cases typically occur in exponentially…

Numerical Analysis · Mathematics 2025-09-09 Robert Plato

Super-resolution as an ill-posed problem has many high-resolution candidates for a low-resolution input. However, the popular $\ell_1$ loss used to best fit the given HR image fails to consider this fundamental property of non-uniqueness in…

Computer Vision and Pattern Recognition · Computer Science 2023-04-11 Xiangyu He , Jian Cheng

We consider abstract operator equations $Fu=y$, where $F$ is a compact linear operator between Hilbert spaces $U$ and $V$, which are function spaces on \emph{closed, finite dimensional Riemannian manifolds}, respectively. This setting is of…

Numerical Analysis · Mathematics 2015-05-28 Nicolas Thorstensen , Otmar Scherzer

We consider the stable approximation of sparse solutions to non-linear operator equations by means of Tikhonov regularization with a subquadratic penalty term. Imposing certain assumptions, which for a linear operator are equivalent to the…

Functional Analysis · Mathematics 2009-12-06 Markus Grasmair , Markus Haltmeier , Otmar Scherzer

This thesis is divided into two parts. In the first part we study completely integrable systems, and their underlying structures, in detail. We study their deformation theory and the different equivalence relations surrounding it. We…

Differential Geometry · Mathematics 2017-12-05 Roy Wang

In this paper we consider inverse problems that are mathematically ill-posed. That is, given some (noisy) data, there is more than one solution that approximately fits the data. In recent years, deep neural techniques that find the most…

Machine Learning · Computer Science 2023-08-28 Moshe Eliasof , Eldad Haber , Eran Treister
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