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We consider linear sparse recovery problems where additional structure regarding the support of the solution is known. The form of the structure considered is non-overlapping sets of indices that each contain part of the support. An…

Numerical Analysis · Mathematics 2021-10-05 Joseph S. Donato , Howard W. Levinson

We explore algorithms and limitations for sparse optimization problems such as sparse linear regression and robust linear regression. The goal of the sparse linear regression problem is to identify a small number of key features, while the…

Machine Learning · Computer Science 2022-06-30 Eric Price , Sandeep Silwal , Samson Zhou

In this letter, we propose an algorithm for recovery of sparse and low rank components of matrices using an iterative method with adaptive thresholding. In each iteration, the low rank and sparse components are obtained using a thresholding…

Numerical Analysis · Computer Science 2017-04-13 Nematollah Zarmehi , Farokh Marvasti

Many model selection algorithms rely on sparse dictionary learning to provide interpretable and physics-based governing equations. The optimization algorithms typically use a hard thresholding process to enforce sparse activations in the…

Optimization and Control · Mathematics 2025-04-30 Derek W. Jollie , Scott G. McCalla

We propose a new iterative greedy algorithm for reconstructions of sparse signals with or without noisy perturbations in compressed sensing. The proposed algorithm, called \emph{subspace thresholding pursuit} (STP) in this paper, is a…

Information Theory · Computer Science 2014-05-22 Chao-Bing Song , Shu-Tao Xia , Xin-Ji Liu

In high-dimensional statistics, variable selection recovers the latent sparse patterns from all possible covariate combinations. This paper proposes a novel optimization method to solve the exact L0-regularized regression problem, which is…

Methodology · Statistics 2022-06-02 Mingzhang Yin , Nhat Ho , Bowei Yan , Xiaoning Qian , Mingyuan Zhou

Compressed sensing aims to undersample certain high-dimensional signals, yet accurately reconstruct them by exploiting signal characteristics. Accurate reconstruction is possible when the object to be recovered is sufficiently sparse in a…

Information Theory · Computer Science 2015-05-13 David L. Donoho , Arian Maleki , Andrea Montanari

Several learning applications require solving high-dimensional regression problems where the relevant features belong to a small number of (overlapping) groups. For very large datasets and under standard sparsity constraints, hard…

Machine Learning · Statistics 2016-05-30 Prateek Jain , Nikhil Rao , Inderjit Dhillon

We consider the problem of in-network compressed sensing from distributed measurements. Every agent has a set of measurements of a signal $x$, and the objective is for the agents to recover $x$ from their collective measurements using only…

Information Theory · Computer Science 2015-06-17 Stacy Patterson , Yonina C. Eldar , Idit Keidar

We develop mask iterative hard thresholding algorithms (mask IHT and mask DORE) for sparse image reconstruction of objects with known contour. The measurements follow a noisy underdetermined linear model common in the compressive sampling…

Machine Learning · Statistics 2011-12-05 Aleksandar Dogandzic , Renliang Gu , Kun Qiu

In statistics, generalized linear models (GLMs) are widely used for modeling data and can expressively capture potential nonlinear dependence of the model's outcomes on its covariates. Within the broad family of GLMs, those with binary…

Statistics Theory · Mathematics 2025-09-04 Namiko Matsumoto , Arya Mazumdar

Sparse inverse covariance selection is a fundamental problem for analyzing dependencies in high dimensional data. However, such a problem is difficult to solve since it is NP-hard. Existing solutions are primarily based on convex…

Numerical Analysis · Computer Science 2018-04-05 Ganzhao Yuan , Haoxian Tan , Wei-Shi Zheng

Intensively growing approach in signal processing and acquisition, the Compressive Sensing approach, allows sparse signals to be recovered from small number of randomly acquired signal coefficients. This paper analyses some of the commonly…

Signal Processing · Electrical Eng. & Systems 2018-02-21 Tamara Koljensic , Caslav Labudovic

Adaptive thresholding methods have proved to yield high SNRs and fast convergence in finding the solution to the Compressed Sensing (CS) problems. Recently, it was observed that the robustness of a class of iterative sparse recovery…

Methodology · Statistics 2016-11-08 Ashkan Esmaeili , Ehsan Asadi , Farokh Marvasti

Sparse optimization is a central problem in machine learning and computer vision. However, this problem is inherently NP-hard and thus difficult to solve in general. Combinatorial search methods find the global optimal solution but are…

Optimization and Control · Mathematics 2020-06-30 Ganzhao Yuan , Li Shen , Wei-Shi Zheng

In a real Hilbert space setting, we study the convergence properties of an inexact gradient algorithm featuring both viscous and Hessian driven damping for convex differentiable optimization. In this algorithm, the gradient evaluation can…

Optimization and Control · Mathematics 2025-09-25 Harsh Choudhary , Jalal Fadili , Vyachelav Kungurtsev

Most stochastic optimization methods use gradients once before discarding them. While variance reduction methods have shown that reusing past gradients can be beneficial when there is a finite number of datapoints, they do not easily extend…

Optimization problems with continuous data appear in, e.g., robust machine learning, functional data analysis, and variational inference. Here, the target function is given as an integral over a family of (continuously) indexed target…

Machine Learning · Computer Science 2023-11-01 Kexin Jin , Jonas Latz , Chenguang Liu , Carola-Bibiane Schönlieb

Structured sparse optimization is an important and challenging problem for analyzing high-dimensional data in a variety of applications such as bioinformatics, medical imaging, social networks, and astronomy. Although a number of structured…

Artificial Intelligence · Computer Science 2016-10-03 Baojian Zhou , Feng Chen

The kernel herding algorithm is used to construct quadrature rules in a reproducing kernel Hilbert space (RKHS). While the computational efficiency of the algorithm and stability of the output quadrature formulas are advantages of this…

Numerical Analysis · Mathematics 2022-07-18 Kazuma Tsuji , Ken'ichiro Tanaka
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