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We prove an algorithmic regularity lemma for $L_p$ regular matrices $(1 < p \leq \infty),$ a class of sparse $\{0,1\}$ matrices which obey a natural pseudorandomness condition. This extends a result of Coja-Oghlan, Cooper and Frieze who…

Combinatorics · Mathematics 2017-05-19 Thodoris Karageorgos , Silouanos Brazitikos

We establish the density of the partial regularity result in the class of continuous viscosity solutions. Given a fully nonlinear equation, we prove the existence of a sequence entitled to the partial regularity result, approximating its…

Analysis of PDEs · Mathematics 2020-10-29 Disson dos Prazeres , Edgard A. Pimentel , Giane C. Rampasso

We study a sparse negative binomial regression (NBR) for count data by showing the non-asymptotic advantages of using the elastic-net estimator. Two types of oracle inequalities are derived for the NBR's elastic-net estimates by using the…

Machine Learning · Statistics 2022-01-11 Huiming Zhang , Jinzhu Jia

Pseudospectral analysis is fundamental for quantifying the sensitivity and transient behavior of nonnormal matrices, yet its computational cost scales cubically with dimension, rendering it prohibitive for large-scale systems. While…

Numerical Analysis · Mathematics 2026-02-03 Vladimir R. Kostic , Dragana Lj. Cvetkovic , Ljiljana Cvetkovic

The paper studies 'good arrangements' (transversality properties) of collections of sets in a normed vector space near a given point in their intersection. We target primal (metric and slope) characterizations of transversality properties…

Optimization and Control · Mathematics 2022-06-17 Nguyen Duy Cuong , Alexander Y. Kruger

We introduce a new consistency-based approach for defining and solving nonnegative/positive matrix and tensor completion problems. The novelty of the framework is that instead of artificially making the problem well-posed in the form of an…

Information Retrieval · Computer Science 2023-10-18 Tung Nguyen , Jeffrey Uhlmann

We present a nonlinear regression framework based on tensor algebra tailored to high dimensional contexts where data is scarce. We exploit algebraic properties of a partial tensor product, namely the m-tensor product, to leverage structured…

Computational Engineering, Finance, and Science · Computer Science 2026-02-10 Rémi Cloarec , Sebastian Rodriguez , Xavier Kestelyn , Francisco Chinesta

Sparse regression and classification estimators that respect group structures have application to an assortment of statistical and machine learning problems, from multitask learning to sparse additive modeling to hierarchical selection.…

Methodology · Statistics 2024-03-11 Ryan Thompson , Farshid Vahid

The convolution of a discrete measure, $x=\sum_{i=1}^ka_i\delta_{t_i}$, with a local window function, $\phi(s-t)$, is a common model for a measurement device whose resolution is substantially lower than that of the objects being observed.…

Optimization and Control · Mathematics 2019-11-27 Armin Eftekhari , Jared Tanner , Andrew Thompson , Bogdan Toader , Hemant Tyagi

We consider surfaces which minimize a nonlocal perimeter functional and we discuss their interior regularity and rigidity properties, in a quantitative and qualitative way, and their (perhaps rather surprising) boundary behavior. We present…

Analysis of PDEs · Mathematics 2016-12-07 Serena Dipierro , Enrico Valdinoci

Motivated by the minimax concave penalty based variable selection in high-dimensional linear regression, we introduce a simple scheme to construct structured semiconvex sparsity promoting functions from convex sparsity promoting functions…

Optimization and Control · Mathematics 2018-09-19 Lixin Shen , Bruce W. Suter , Erin E. Tripp

We study a class of signomials whose positive support is the set of vertices of a simplex and which may have several negative support points in the simplex. Various groups of authors have provided an exact characterization for the global…

Combinatorics · Mathematics 2026-02-11 Gennadiy Averkov , Jonas Ellwanger , Thorsten Theobald , Timo de Wolff

Sparsity and rank functions are important ways of regularizing under-determined linear systems. Optimization of the resulting formulations is made difficult since both these penalties are non-convex and discontinuous. The most common remedy…

Optimization and Control · Mathematics 2019-01-01 Carl Olsson , Marcus Carlsson , Daniele Gerosa

We develop a stability theory for two-dimensional periodic traveling waves of general parabolic systems, possibly including conservation laws. In particular, we identify a diffusive spectral stability assumption and prove that it implies…

Analysis of PDEs · Mathematics 2025-08-07 L. Miguel Rodrigues , Aric Wheeler

The purpose of this paper is to provide tools for analyzing the compactness of sequences in Sobolev spaces, in particular if the sequence gets mapped onto a compact set by some nonlinear operator. Here, our focus lies on a very general…

Analysis of PDEs · Mathematics 2007-11-07 Stefan Krömer , Markus Lilli

Sparsity promoting functions (SPFs) are commonly used in optimization problems to find solutions which are assumed or desired to be sparse in some basis. For example, the l1-regularized variation model and the Rudin-Osher-Fatemi total…

Optimization and Control · Mathematics 2019-09-13 Lixin Shen , Bruce W. Suter , Erin E. Tripp

We use supersymmetry to calculate exact spectral densities for a class of complex random matrix models having the form $M=S+LXR$, where $X$ is a random noise part $X$ and $S,L,R$ are fixed structure parts. This is a certain version of the…

Mathematical Physics · Physics 2016-11-02 Jacek Grela , Thomas Guhr

Among reduced Noetherian prime characteristic commutative rings, we prove that a regular ring is precisely one where finite intersection of ideals commutes with taking bracket powers. However, reducedness is essential for this equivalence.…

Commutative Algebra · Mathematics 2021-02-23 Neil Epstein

Repeated measurements are common in many fields, where random variables are observed repeatedly across different subjects. Such data have an underlying hierarchical structure, and it is of interest to learn covariance/correlation at…

Methodology · Statistics 2023-06-13 Sunpeng Duan , Guo Yu , Juntao Duan , Yuedong Wang

We consider projection algorithms for solving (nonconvex) feasibility problems in Euclidean spaces. Of special interest are the Method of Alternating Projections (MAP) and the Douglas-Rachford or Averaged Alternating Reflection Algorithm…

Optimization and Control · Mathematics 2014-03-17 Robert Hesse , D. Russell Luke
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