DOI10.1002/cpa.20124zbMath1098.94009arXivmath/0503066OpenAlexW2164452299WikidataQ56688957 ScholiaQ56688957MaRDI QIDQ5486267
Emmanuel J. Candès, Justin Romberg, Terence C. Tao
Publication date: 6 September 2006
Published in: Communications on Pure and Applied Mathematics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/math/0503066
Dynamical sampling,
Structure dependent sampling in compressed sensing: theoretical guarantees for tight frames,
On the interval of fluctuation of the singular values of random matrices,
The Moreau envelope based efficient first-order methods for sparse recovery,
Large sparse signal recovery by conjugate gradient algorithm based on smoothing technique,
Optimal Experimental Design for Inverse Problems with State Constraints,
A Novel Compressed Sensing Scheme for Photoacoustic Tomography,
Recursive SURE for iterative reweighted least square algorithms,
Faster sparse multivariate polynomial interpolation of straight-line programs,
THE RESTRICTED ISOMETRY PROPERTY FOR SIGNAL RECOVERY WITH COHERENT TIGHT FRAMES,
A Survey of Compressed Sensing,
Recovering Structured Signals in Noise: Least-Squares Meets Compressed Sensing,
Quantization and Compressive Sensing,
Two Algorithms for Compressed Sensing of Sparse Tensors,
Compressive Classification: Where Wireless Communications Meets Machine Learning,
An Augmented Lagrangian Method for $\ell_{1}$-Regularized Optimization Problems with Orthogonality Constraints,
Sparse signal recovery via non-convex optimization and overcomplete dictionaries,
Deterministic construction of compressed sensing matrices with characters over finite fields,
Fiber Orientation Distribution Estimation Using a Peaceman--Rachford Splitting Method,
Multicontrast MRI Reconstruction with Structure-Guided Total Variation,
Sparsity and Nullity: Paradigms for Analysis Dictionary Learning,
Harmonic Analysis and Random Schrödinger Operators,
A Weighted Difference of Anisotropic and Isotropic Total Variation Model for Image Processing,
Suprema of Chaos Processes and the Restricted Isometry Property,
Nonlinear regularization techniques for seismic tomography,
The recovery of complex sparse signals from few phaseless measurements,
\(\ell^1\)-analysis minimization and generalized (co-)sparsity: when does recovery succeed?,
Smoothing strategy along with conjugate gradient algorithm for signal reconstruction,
A novel regularization based on the error function for sparse recovery,
Compressive statistical learning with random feature moments,
A proximal algorithm with backtracked extrapolation for a class of structured fractional programming,
Compressive sensing based machine learning strategy for characterizing the flow around a cylinder with limited pressure measurements,
Performance analysis of the compressed distributed least squares algorithm,
The finite steps of convergence of the fast thresholding algorithms with \(f\)-feedbacks in compressed sensing,
Improved bounds for the RIP of Subsampled Circulant matrices,
Compressive sensing Petrov-Galerkin approximation of high-dimensional parametric operator equations,
A nonconvex approach to low-rank matrix completion using convex optimization,
Explicit RIP matrices: an update,
Sparse estimation via lower-order penalty optimization methods in high-dimensional linear regression,
Comparison of the performance and reliability between improved sampling strategies for polynomial chaos expansion,
Compressed sensing of low-rank plus sparse matrices,
Sparse Approximation using $\ell_1-\ell_2$ Minimization and Its Application to Stochastic Collocation,
Unnamed Item,
Retraction-based first-order feasible methods for difference-of-convex programs with smooth inequality and simple geometric constraints,
Applied harmonic analysis and data science. Abstracts from the workshop held November 28 -- December 4, 2021 (hybrid meeting),
Adaptive iterative hard thresholding for low-rank matrix recovery and rank-one measurements,
On unique recovery of finite-valued integer signals and admissible lattices of sparse hypercubes,
Stochastic Collocation Methods via $\ell_1$ Minimization Using Randomized Quadratures,
Solving saddle point problems: a landscape of primal-dual algorithm with larger stepsizes,
Compressive Sensing,
Conic formulation of QPCCs applied to truly sparse QPs,
A compressed sensing approach to interpolation of fractional Brownian trajectories for a single particle tracking experiment,
On the sparsity of Lasso minimizers in sparse data recovery,
Acoustic Source Localization and Deconvolution-Based Separation,
An Introduction to Compressed Sensing,
Quantized Compressed Sensing: A Survey,
On Reconstructing Functions from Binary Measurements,
Classification Scheme for Binary Data with Extensions,
A note on the minimization of a Tikhonov functional with ℓ1-penalty,
Heuristic discrepancy principle for variational regularization of inverse problems,
Reconstruction of sparse connectivity in neural networks from spike train covariances,
Analytic solution to variance optimization with no short positions,
A theoretical analysis of L1 regularized Poisson likelihood estimation,
Randomized pick-freeze for sparse Sobol indices estimation in high dimension,
Introduction,
Large-Scale Inverse Problems in Imaging,
Mathematics of Electron Tomography,
A Compressive Sensing Based Analysis of Anomalies in Generalized Linear Models,
Nesterov's algorithm solving dual formulation for compressed sensing,
Reprint of ``Nesterov's algorithm solving dual formulation for compressed sensing, Variations on a theorem of Candès, Romberg and Tao, Low Complexity Regularization of Linear Inverse Problems, Noise-Shaping Quantization Methods for Frame-Based and Compressive Sampling Systems, The stochastic properties of \(\ell^1\)-regularized spherical Gaussian fields, Necessary and sufficient conditions for linear convergence of ℓ1-regularization, Surface Reconstruction via L 1-Minimization, Fast and RIP-optimal transforms, Robust multi-image processing with optimal sparse regularization, Classification of Spatiotemporal Data via Asynchronous Sparse Sampling: Application to Flow around a Cylinder, A note on linear function approximation using random projections, Compressed sensing and best 𝑘-term approximation, A Mixture of Wisharts (MOW) Model for Multifiber Reconstruction, Exact reconstruction of the nonnegative measures using model sets, COMPRESSED SENSING BY ITERATIVE THRESHOLDING OF GEOMETRIC WAVELETS: A COMPARING STUDY, CURVELET-WAVELET REGULARIZED SPLIT BREGMAN ITERATION FOR COMPRESSED SENSING, Sparse Recovery with Partial Support Knowledge, Network reconstruction of social networks based on the public information, Identification of interactions in fractional-order systems with high dimensions, Concave programming for finding sparse solutions to problems with convex constraints, ADAPTIVE DATA ANALYSIS VIA SPARSE TIME-FREQUENCY REPRESENTATION, $L_p$-norm Regularization Algorithms for Optimization Over Permutation Matrices, Iteratively reweighted least squares minimization for sparse recovery, Additive Combinatorics: With a View Towards Computer Science and Cryptography—An Exposition, One-Bit Compressed Sensing by Linear Programming, On perturbed steepest descent methods with inexact line search for bilevel convex optimization, SPARSE RECONSTRUCTION OF HARDY SIGNAL AND APPLICATIONS TO TIME-FREQUENCY DISTRIBUTION, Sparse Representation of Signals in Hardy Space, Design of structured dynamic output-feedback controllers for interconnected systems, Truncated $l_{1-2}$ Models for Sparse Recovery and Rank Minimization, Primal–dual first-order methods for a class of cone programming, A regularizing multilevel approach for nonlinear inverse problems, A general family of trimmed estimators for robust high-dimensional data analysis, Sparse signals recovered by non-convex penalty in quasi-linear systems, Learning and sparse control of multiagent systems, Polynomial-exponential decomposition from moments, On the approximate discrete KLT of fractional Brownian motion and applications, Iteratively weighted thresholding homotopy method for the sparse solution of underdetermined linear equations, Sparse recovery via nonconvex regularized \(M\)-estimators over \(\ell_q\)-balls, Signal recovery under cumulative coherence, Some sharp performance bounds for least squares regression with \(L_1\) regularization, Computation of sparse and dense equilibrium strategies of evolutionary games, Sparsity in penalized empirical risk minimization, Surface inpainting with sparsity constraints, Observable dictionary learning for high-dimensional statistical inference, Random sampling of sparse trigonometric polynomials, A sublinear algorithm for the recovery of signals with sparse Fourier transform when many samples are missing, Sparse approximate solution of fitting surface to scattered points by MLASSO model, Compressed sensing for real measurements of quaternion signals, Expander \(\ell_0\)-decoding, PROMP: a sparse recovery approach to lattice-valued signals, A conjugate subgradient algorithm with adaptive preconditioning for the least absolute shrinkage and selection operator minimization, Sparsity enabled cluster reduced-order models for control, RBF-network based sparse signal recovery algorithm for compressed sensing reconstruction, The matrix splitting based proximal fixed-point algorithms for quadratically constrained \(\ell_{1}\) minimization and Dantzig selector, Adaptive projected gradient thresholding methods for constrained \(l_0\) problems, Online fault diagnosis for nonlinear power systems, Geometric separation of singularities using combined multiscale dictionaries, Stability of the elastic net estimator, Approximating sampled sinusoids and multiband signals using multiband modulated DPSS dictionaries, Noisy 1-bit compressive sensing: models and algorithms, From compression to compressed sensing, A strong restricted isometry property, with an application to phaseless compressed sensing, Direct data domain STAP using sparse representation of clutter spectrum, Smoothed \(\ell_1\)-regularization-based line search for sparse signal recovery, Restricted isometry property of matrices with independent columns and neighborly polytopes by random sampling, Explicit constructions of RIP matrices and related problems, Fast state-space methods for inferring dendritic synaptic connectivity, Exact simultaneous recovery of locations and structure from known orientations and corrupted point correspondences, Learning data discretization via convex optimization, Geometric separation in \(\mathbb{R}^3\), A new nonconvex approach to low-rank matrix completion with application to image inpainting, Democracy in action: quantization, saturation, and compressive sensing, Compressed sensing with structured sparsity and structured acquisition, Recovery of signals under the condition on RIC and ROC via prior support information, A computational study of the role of spatial receptive field structure in processing natural and non-natural scenes, Sparse signal reconstruction based on multiparameter approximation function with smoothed \(\ell_0\) norm, Beyond sparsity: the role of \(L_{1}\)-optimizer in pattern classification, Image decoding optimization based on compressive sensing, Roles of clustering coefficient for the network reconstruction, The restricted isometry property and its implications for compressed sensing, Linear total variation approximate regularized nuclear norm optimization for matrix completion, Signal recovery under mutual incoherence property and oracle inequalities, Applied harmonic analysis and data processing. Abstracts from the workshop held March 25--31, 2018, On monotone and primal-dual active set schemes for \(\ell^p\)-type problems, \(p \in (0,1\)], Recovery analysis for weighted mixed \(\ell_2 / \ell_p\) minimization with \(0 < p \leq 1\), Linear versus non-linear acquisition of step-functions, Noise folding in completely perturbed compressed sensing, A remark on compressed sensing, Iterative thresholding algorithms, Data-based prediction and causality inference of nonlinear dynamics, Fast L1-L2 minimization via a proximal operator, Nonlinear frames and sparse reconstructions in Banach spaces, A statistical approach to the problem of restoring damaged and contaminated images, Spectral dynamics and regularization of incompletely and irregularly measured data, Restricted \(p\)-isometry property and its application for nonconvex compressive sensing, Sparsity in time-frequency representations, Sparse approximate solution of partial differential equations, Combinatorial sublinear-time Fourier algorithms, On support sizes of restricted isometry constants, L1Packv2: A Mathematica package for minimizing an \(\ell _{1}\)-penalized functional, Sparse recovery by non-convex optimization - instance optimality, On the size of incoherent systems, Gelfand numbers related to structured sparsity and Besov space embeddings with small mixed smoothness, Enhancing \(\ell_1\)-minimization estimates of polynomial chaos expansions using basis selection, Enhancing sparsity of Hermite polynomial expansions by iterative rotations, Iterative hard thresholding for compressed sensing, Instance-optimality in probability with an \(\ell _1\)-minimization decoder, Three novel edge detection methods for incomplete and noisy spectral data, Accelerated projected gradient method for linear inverse problems with sparsity constraints, Atoms of all channels, unite! Average case analysis of multi-channel sparse recovery using greedy algorithms, Enhancing sparsity by reweighted \(\ell _{1}\) minimization, Image reconstruction for diffuse optical tomography based on radiative transfer equation, Fast proximity-gradient algorithms for structured convex optimization problems, A null space analysis of the \(\ell_1\)-synthesis method in dictionary-based compressed sensing, Phase retrieval for sparse signals, Properties and iterative methods for the lasso and its variants, GPU accelerated greedy algorithms for compressed sensing, Matrix-free interior point method for compressed sensing problems, CoSaMP: Iterative signal recovery from incomplete and inaccurate samples, Sparsest solutions of underdetermined linear systems via \( \ell _q\)-minimization for \(0<q\leqslant 1\), Random sampling of sparse trigonometric polynomials. II: Orthogonal matching pursuit versus basis pursuit, Random projections of smooth manifolds, Compressive sensing for subsurface imaging using ground penetrating radar, Sparse solutions to underdetermined Kronecker product systems, A simple proof of the restricted isometry property for random matrices, Uniform uncertainty principle for Bernoulli and subgaussian ensembles, The \(\ell_{2,q}\) regularized group sparse optimization: lower bound theory, recovery bound and algorithms, Preserving injectivity under subgaussian mappings and its application to compressed sensing, Complete set of translation invariant measurements with Lipschitz bounds, Mixed linear system estimation and identification, Use of EM algorithm for data reduction under sparsity assumption, Refined analysis of sparse MIMO radar, A LONE code for the sparse control of quantum systems, A novel coherence reduction method in compressed sensing for DOA estimation, Improved sparse Fourier approximation results: Faster implementations and stronger guarantees, Recovery analysis for weighted \(\ell_{1}\)-minimization using the null space property, Recovery error analysis of noisy measurement in compressed sensing, Explicit universal sampling sets in finite vector spaces, Compressed sensing for finite-valued signals, Adaptive step-size matching pursuit algorithm for practical sparse reconstruction, The modified accelerated Bregman method for regularized basis pursuit problem, A primal Douglas-Rachford splitting method for the constrained minimization problem in compressive sensing, Quantization of compressive samples with stable and robust recovery, Newly deterministic construction of compressed sensing matrices via singular linear spaces over finite fields, L1-norm minimization for multi-dimensional signals based on geometric algebra, A refined convergence analysis of \(\mathrm{pDCA}_{e}\) with applications to simultaneous sparse recovery and outlier detection, Empirical risk minimization: probabilistic complexity and stepsize strategy, A nonconvex model with minimax concave penalty for image restoration, A simple homotopy proximal mapping algorithm for compressive sensing, Capturing ridge functions in high dimensions from point queries, Adaptive decomposition-based evolutionary approach for multiobjective sparse reconstruction, Effective zero-norm minimization algorithms for noisy compressed sensing, Geological facies recovery based on weighted \(\ell_1\)-regularization, Complexity of approximation of functions of few variables in high dimensions, Deletion correcting codes meet the Littlewood-Offord problem, Recovering an unknown signal completely submerged in strong noise by a new stochastic resonance method, Stability and robustness of \(\ell_1\)-minimizations with Weibull matrices and redundant dictionaries, Sparse linear regression from perturbed data, Sparse modeling approach to obtaining the shear viscosity from smeared correlation functions, Asymptotic risk and phase transition of \(l_1\)-penalized robust estimator, Inverse potential problems for divergence of measures with total variation regularization, Exact recovery of non-uniform splines from the projection onto spaces of algebraic polynomials, Concentration of \(S\)-largest mutilated vectors with \(\ell_p\)-quasinorm for \(0<p\leq 1\) and its applications, Fixed-point proximity algorithms solving an incomplete Fourier transform model for seismic wavefield modeling, Optimized projections for compressed sensing via rank-constrained nearest correlation matrix, Sparse regression: scalable algorithms and empirical performance, Rejoinder: ``Sparse regression: scalable algorithms and empirical performance,
The complexity results of the sparse optimization problems and reverse convex optimization problems,
Discrete optimization methods for group model selection in compressed sensing,
Wave atoms and sparsity of oscillatory patterns,
Inexact operator splitting method for monotone inclusion problems,
Beyond coherence: Recovering structured time-frequency representations,
Efficient sensing of von Kármán vortices using compressive sensing,
The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).,
A compressed-sensing approach for closed-loop optimal control of nonlinear systems,
Sparsity- and continuity-promoting seismic image recovery with curvelet frames,
The nonnegative zero-norm minimization under generalized \(Z\)-matrix measurement,
Sharp RIP bound for sparse signal and low-rank matrix recovery,
Compressed sensing with sparse binary matrices: instance optimal error guarantees in near-optimal time,
Geometric separation by single-pass alternating thresholding,
Convergence of projected Landweber iteration for matrix rank minimization,
Stable restoration and separation of approximately sparse signals,
Sparse recovery with coherent tight frames via analysis Dantzig selector and analysis LASSO,
An approximate sparsity model for inpainting,
Convergence of a data-driven time-frequency analysis method,
Robust dequantized compressive sensing,
Solving \(\ell_0\)-penalized problems with simple constraints via the Frank-Wolfe reduced dimension method,
A shrinkage-thresholding projection method for sparsest solutions of LCPs,
Sparse matrices in frame theory,
Extragradient thresholding methods for sparse solutions of co-coercive ncps,
Distributed reconstruction via alternating direction method,
Prior image guided undersampled dual energy reconstruction with piecewise polynomial function constraint,
Sparse-view ultrasound diffraction tomography using compressed sensing with nonuniform FFT,
3D alternating direction TV-based cone-beam CT reconstruction with efficient GPU implementation,
Stability and instance optimality for Gaussian measurements in compressed sensing,
Data science, big data and statistics,
Iterative re-weighted least squares algorithm for \(l_p\)-minimization with tight frame and \(0 < p \leq 1\),
A theoretical investigation of Brockett's ensemble optimal control problems,
Construction of highly redundant incoherent unit norm tight frames as a union of orthonormal bases,
On convex envelopes and regularization of non-convex functionals without moving global minima,
On a monotone scheme for nonconvex nonsmooth optimization with applications to fracture mechanics,
Sparse regularization for semi-supervised classification,
On the number of harmonic frames,
Signal reconstruction by conjugate gradient algorithm based on smoothing \(l_1\)-norm,
An adaptive family of projection methods for constrained monotone nonlinear equations with applications,
Analysis of compressed distributed adaptive filters,
An efficient privacy-preserving compressive data gathering scheme in WSNs,
Sparse reconstruction of log-conductivity in current density impedance tomography,
Deterministic constructions of compressed sensing matrices based on codes,
On the strong restricted isometry property of Bernoulli random matrices,
Sparse reconstruction with multiple Walsh matrices,
Frames induced by the action of continuous powers of an operator,
Enabling numerically exact local solver for waveform inversion -- a low-rank approach,
Compressed-sensing-based gradient reconstruction for ghost imaging,
Sampling strategies for uncertainty reduction in categorical random fields: formulation, mathematical analysis and application to multiple-point simulations,
Joint-block-sparsity for efficient 2-D DOA estimation with multiple separable observations,
Optimal RIP bounds for sparse signals recovery via \(\ell_p\) minimization,
Sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit,
Quantized compressed sensing for random circulant matrices,
A modified primal-dual method with applications to some sparse recovery problems,
Outlier deletion based improvement on the stomp algorithm for sparse solution of large-scale underdetermined problems,
Weaker regularity conditions and sparse recovery in high-dimensional regression,
Generalized cross validation in variable selection with and without shrinkage,
Near oracle performance and block analysis of signal space greedy methods,
An asymptotic existence result on compressed sensing matrices,
Greedy signal space methods for incoherence and beyond,
New analysis of manifold embeddings and signal recovery from compressive measurements,
A modified Newton projection method for \(\ell _1\)-regularized least squares image deblurring,
Adaptive restart for accelerated gradient schemes,
Sparse proximal support vector machine with a specialized interior-point method,
\(\alpha\)-Molecules,
A geometrical stability condition for compressed sensing,
Statistical consistency of coefficient-based conditional quantile regression,
SLOPE is adaptive to unknown sparsity and asymptotically minimax,
Stable signal recovery from phaseless measurements,
Compressed sensing and dynamic mode decomposition,
Deterministic convolutional compressed sensing matrices,
The non-convex sparse problem with nonnegative constraint for signal reconstruction,
Sharp MSE bounds for proximal denoising,
Point source super-resolution via non-convex \(L_1\) based methods,
1-bit compressive sensing: reformulation and RRSP-based sign recovery theory,
Regularity properties of non-negative sparsity sets,
Robust sparse phase retrieval made easy,
A weighted \(\ell_1\)-minimization approach for sparse polynomial chaos expansions,
Reconstruction of smooth and discontinuous components of solutions to linear ill-posed problems,
Block sparse recovery via mixed \(l_2/l_1\) minimization,
The essential ability of sparse reconstruction of different compressive sensing strategies,
Influence factors of sparse microwave imaging radar system performance: approaches to waveform design and platform motion analysis,
Reweighted minimization model for MR image reconstruction with split Bregman method,
Sparse microwave imaging: principles and applications,
Sparse SAR imaging based on \(L_{1/2}\) regularization,
Compressed sensing SAR imaging based on sparse representation in fractional Fourier domain,
Recovery of high-dimensional sparse signals via \(\ell_1\)-minimization,
The restricted isometry property for time-frequency structured random matrices,
ParNes: A rapidly convergent algorithm for accurate recovery of sparse and approximately sparse signals,
Sparsity and non-Euclidean embeddings,
Sparse recovery under matrix uncertainty,
Approximation accuracy, gradient methods, and error bound for structured convex optimization,
On exact recovery of sparse vectors from linear measurements,
Multiple suboptimal solutions for prediction rules in gene expression data,
Theory of compressive sensing via \(\ell_1\)-minimization: a non-RIP analysis and extensions,
The Gelfand widths of \(\ell_p\)-balls for \(0 < p \leq 1\),
Random sampling of bandlimited functions,
Effective band-limited extrapolation relying on Slepian series and \(\ell^1\) regularization,
Exact minimum rank approximation via Schatten \(p\)-norm minimization,
Approximation of solutions with singularities of various types for linear ill-posed problems,
Remote sensing via \(\ell_1\)-minimization,
Analysis of discrete \(L^2\) projection on polynomial spaces with random evaluations,
Nonconvex compressed sampling of natural images and applications to compressed MR imaging,
Approximation of functions of few variables in high dimensions,
Restricted isometries for partial random circulant matrices,
Recovery of sparsest signals via \(\ell^q \)-minimization,
Stable direction recovery in single-index models with a diverging number of predictors,
Coupling the gradient method with a general exterior penalization scheme for convex minimization,
Sparse Legendre expansions via \(\ell_1\)-minimization,
The residual method for regularizing ill-posed problems,
Sparsity enforcing edge detection method for blurred and noisy Fourier data,
A novel sparsity reconstruction method from Poisson data for 3D bioluminescence tomography,
On verifiable sufficient conditions for sparse signal recovery via \(\ell_{1}\) minimization,
Projected gradient iteration for nonlinear operator equation,
Learning functions of few arbitrary linear parameters in high dimensions,
Exact reconstruction using Beurling minimal extrapolation,
Perturbations of measurement matrices and dictionaries in compressed sensing,
Average best \(m\)-term approximation,
Lagrangian-penalization algorithm for constrained optimization and variational inequalities,
Particle swarm optimization of compression measurement for signal detection,
Iterative design of concentration factors for jump detection,
Guaranteed clustering and biclustering via semidefinite programming,
Stable recovery of sparse signals via \(\ell_p\)-minimization,
Compressed sensing with preconditioning for sparse recovery with subsampled matrices of Slepian prolate functions,
A multiplicative Nakagami speckle reduction algorithm for ultrasound images,
First-order optimality condition of basis pursuit denoise problem,
Compressed classification learning with Markov chain samples,
Sparse recovery on Euclidean Jordan algebras,
Nonmonotone Barzilai-Borwein gradient algorithm for \(\ell_1\)-regularized nonsmooth minimization in compressive sensing,
Sparse time-frequency representation of nonlinear and nonstationary data,
Compressed sensing with coherent tight frames via \(l_q\)-minimization for \(0 < q \leq 1\),
Learning circulant sensing kernels,
A variable fixing version of the two-block nonlinear constrained Gauss-Seidel algorithm for \(\ell_1\)-regularized least-squares,
A consistent and stable approach to generalized sampling,
\(l_p\)-recovery of the most significant subspace among multiple subspaces with outliers,
Log-concavity and strong log-concavity: a review,
\(L_1\)-penalization in functional linear regression with subgaussian design,
Bregman iteration algorithm for sparse nonnegative matrix factorizations via alternating \(l_1\)-norm minimization,
Separate reconstruction of solution components with singularities of various types for linear operator equations of the first kind,
Extreme point inequalities and geometry of the rank sparsity ball,
A half thresholding projection algorithm for sparse solutions of LCPs,
Bayesian signal detection with compressed measurements,
Kernel sparse representation for time series classification,
Decomposable norm minimization with proximal-gradient homotopy algorithm,
Orthogonal matching pursuit under the restricted isometry property,
Sparse decomposition by iterating Lipschitzian-type mappings,
The sparsest solutions to \(Z\)-tensor complementarity problems,
Compressed sensing from a harmonic analysis point of view,
Sparse recovery under weak moment assumptions,
Sparse signal recovery using a new class of random matrices,
Data based identification and prediction of nonlinear and complex dynamical systems,
Tightness of the maximum likelihood semidefinite relaxation for angular synchronization,
Dualizable shearlet frames and sparse approximation,
The null space property for sparse recovery from multiple measurement vectors,
Observability for initial value problems with sparse initial data,
Two-dimensional random projection,
Compressed sensing with coherent and redundant dictionaries,
Compressive wave computation,
A box constrained gradient projection algorithm for compressed sensing,
Geometric median and robust estimation in Banach spaces,
Discovering governing equations from data by sparse identification of nonlinear dynamical systems,
Empirical average-case relation between undersampling and sparsity in X-ray CT,
Periodic spline-based frames for image restoration,
Strong convergence of a modified proximal algorithm for solving the lasso,
Volumes of unit balls of mixed sequence spaces,
Effect of sensing matrices on quality index parameters for block sparse bayesian learning-based EEG compressive sensing,
Block-sparse compressed sensing: non-convex model and iterative re-weighted algorithm,
Unnamed Item,
Sparsity Constrained Estimation in Image Processing and Computer Vision,
Stable and robust $\ell_p$-constrained compressive sensing recovery via robust width property,
A General Theory of Singular Values with Applications to Signal Denoising,
Extracting Sparse High-Dimensional Dynamics from Limited Data,
Sparsest representations and approximations of an underdetermined linear system,
Optimal Injectivity Conditions for Bilinear Inverse Problems with Applications to Identifiability of Deconvolution Problems,
\(\mathrm{L_1RIP}\)-based robust compressed sensing,
One-bit gridless DOA estimation with multiple measurements exploiting accelerated proximal gradient algorithm,
Algebraic compressed sensing,
Almost optimality of orthogonal super greedy algorithms for incoherent dictionaries,
Doubly majorized algorithm for sparsity-inducing optimization problems with regularizer-compatible constraints,
Sparse optimization via vector \(k\)-norm and DC programming with an application to feature selection for support vector machines,
Framework for segmented threshold \(\ell_0\) gradient approximation based network for sparse signal recovery,
On sparse approximations of solutions to linear systems with orthogonal matrices,
Block-sparse recovery and rank minimization using a weighted \(l_p-l_q\) model,
A Critical Review of LASSO and Its Derivatives for Variable Selection Under Dependence Among Covariates,
Statistical guarantees for regularized neural networks,
Sparse approximation over the cube,
Deep-OSG: deep learning of operators in semigroup,
A generalized conditional gradient method for dynamic inverse problems with optimal transport regularization,
A unified approach to uniform signal recovery from nonlinear observations,
A dual active set method for \(\ell1\)-regularized problem,
From irrevocably modulated filtrations to dynamical equations over random networks,
Sparse regression for low-dimensional time-dynamic varying coefficient models with application to air quality data,
Sequential edge detection using joint hierarchical Bayesian learning,
Selected Topics of the Past Thirty Years in Ocean Acoustics,
Kernel functions embed into the autoencoder to identify the sparse models of nonlinear dynamics,
A wonderful triangle in compressed sensing,
Computational Aspects of Constrained L 1-L 2 Minimization for Compressive Sensing,
An \(l_0\)-norm based color image deblurring model under mixed random-valued impulse and Gaussian noise,
Sparse subsampling of flow measurements for finite-time Lyapunov exponent in domains with obstacles,
Distributed Decoding From Heterogeneous 1-Bit Compressive Measurements,
Compressive phase retrieval: Optimal sample complexity with deep generative priors,
Sampling rates for \(\ell^1\)-synthesis,
\(\boldsymbol{L_1-\beta L_q}\) Minimization for Signal and Image Recovery,
Discrete-time scale-shift approach for wavelet construction and analysis,
LASSO Reloaded: A Variational Analysis Perspective with Applications to Compressed Sensing,
Importance sampling in signal processing applications,
A Mass-Shifting Phenomenon of Truncated Multivariate Normal Priors,
Multi-task sparse identification for closed-loop systems with general observation sequences,
Inducing sparsity via the horseshoe prior in imaging problems,
A reduced half thresholding algorithm,
Accelerating inexact successive quadratic approximation for regularized optimization through manifold identification,
Modewise operators, the tensor restricted isometry property, and low-rank tensor recovery,
Reconstruction of sparse recurrent connectivity and inputs from the nonlinear dynamics of neuronal networks,
Nonoverlapping convex polytopes with vertices in a Boolean cube and other problems in coding theory,
On the Convergence of Stochastic Gradient Descent for Linear Inverse Problems in Banach Spaces,
Noisy linear inverse problems under convex constraints: exact risk asymptotics in high dimensions,
Stable Recovery of Sparsely Corrupted Signals Through Justice Pursuit De-Noising,
Representation recovery via \(L_1\)-norm minimization with corrupted data,
Stochastic Collocation vial1-Minimisation on Low Discrepancy Point Sets with Application to Uncertainty Quantification,
Foundations of Gauge and Perspective Duality,
Unnamed Item,
Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery,
Compressive time-of-flight 3D imaging using block-structured sensing matrices,
Regularization of inverse problems via time discrete geodesics in image spaces,
Generalized Mercer Kernels and Reproducing Kernel Banach Spaces,
Consistent parameter estimation for Lasso and approximate message passing,
Sparse Solutions of Linear Diophantine Equations,
A SPREAD-RETURN MEAN-REVERTING MODEL FOR CREDIT SPREAD DYNAMICS,
Unnamed Item,
Unnamed Item,
Unnamed Item,
Unnamed Item,
Compressed sensing,
Bayesian computation: a summary of the current state, and samples backwards and forwards,
Multiscale Approximation,
Sparse reconstructions from few noisy data: analysis of hierarchical Bayesian models with generalized gamma hyperpriors,
How entropic regression beats the outliers problem in nonlinear system identification,
Weak Stability of ℓ1-Minimization Methods in Sparse Data Reconstruction,
Imaging with highly incomplete and corrupted data,
Linearly involved generalized Moreau enhanced models and their proximal splitting algorithm under overall convexity condition,
An improved bound of cumulative coherence for signal recovery,
Sparse System Identification in Pairs of Pulse and Takenaka--Malmquist Bases,
Unnamed Item,
An image reconstruction model regularized by edge-preserving diffusion and smoothing for limited-angle computed tomography,
Detecting unstable periodic orbits based only on time series: When adaptive delayed feedback control meets reservoir computing,
Predicting Time Series from Short-Term High-Dimensional Data,
Computing non-negative tensor factorizations,
Quasi-linear Compressed Sensing,
Minimization of $\ell_{1-2}$ for Compressed Sensing,
Stochastic Collocation Algorithms Using $l_1$-Minimization for Bayesian Solution of Inverse Problems,
The Restricted Isometry Property of Subsampled Fourier Matrices,
New Restricted Isometry Property Analysis for $\ell_1-\ell_2$ Minimization Methods,
Multiscale Factorization of the Wave Equation with Application to Compressed Sensing Photoacoustic Tomography,
A Weighted Difference of Anisotropic and Isotropic Total Variation for Relaxed Mumford--Shah Color and Multiphase Image Segmentation,
Unnamed Item,
Compressive Sampling for Energy Spectrum Estimation of Turbulent Flows,
A New Computational Method for the Sparsest Solutions to Systems of Linear Equations,
Weighted $\ell_p$-Minimization for Sparse Signal Recovery under Arbitrary Support Prior,
Sparse Solutions of a Class of Constrained Optimization Problems,
On the Generation of Sampling Schemes for Magnetic Resonance Imaging,
Reed-Muller Codes,
Dynamical sampling in multiply generated shift-invariant spaces,
Nonlinear approximation spaces for inverse problems,
A Generalized Sampling and Preconditioning Scheme for Sparse Approximation of Polynomial Chaos Expansions,
Research on IQ imbalance error of orthogonal alternating sampling,
Sparse high-dimensional linear regression. Estimating squared error and a phase transition,
Stable high-order cubature formulas for experimental data,
On the Fourier transform of a quantitative trait: implications for compressive sensing,
Sequential image recovery from noisy and under-sampled Fourier data,
Compressive sensing of high betweenness centrality nodes in networks,
Sparse signal recovery via generalized Gaussian function,
Bias versus non-convexity in compressed sensing,
Binomiality testing and computing sparse polynomials via witness sets,
The restricted isometry property of block diagonal matrices for group-sparse signal recovery,
Inverting incomplete Fourier transforms by a sparse regularization model and applications in seismic wavefield modeling,
Gradient projection Newton pursuit for sparsity constrained optimization,
The springback penalty for robust signal recovery,
On the robustness of noise-blind low-rank recovery from rank-one measurements,
Real-valued group testing for quantitative molecular assays,
How can we identify the sparsity structure pattern of high-dimensional data: an elementary statistical analysis to interpretable machine learning,
Monte Carlo simulation of sensitivity functions for few-view computed tomography of strongly absorbing media,
Generalizing CoSaMP to signals from a union of low dimensional linear subspaces,
Isotropic sparse regularization for spherical harmonic representations of random fields on the sphere,
Holographic sensing,
Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques,
Self-adaptive image reconstruction inspired by insect compound eye mechanism,
A smoothing neural network for minimization \(l_1\)-\(l_p\) in sparse signal reconstruction with measurement noises,
CONFIGR: a vision-based model for long-range figure completion,
Robust estimation for an inverse problem arising in multiview geometry,
An inexact alternating directions algorithm for constrained total variation regularized compressive sensing problems,
Sparse signal reconstruction via the approximations of \(\ell_0\) quasinorm,
A smoothing method for sparse optimization over convex sets,
Analysis non-sparse recovery for relaxed ALASSO,
Compressed sensing and matrix completion with constant proportion of corruptions,
Streaming graph computations with a helpful advisor,
Compressive total variation for image reconstruction and restoration,
Random sampling in reproducing kernel subspaces of \(L^p(\mathbb{R}^n)\),
The extensions of convergence rates of Kaczmarz-type methods,
Deterministic construction of compressed sensing matrices from constant dimension codes,
Fast overcomplete dictionary construction with probabilistic guarantees,
Sobolev duals for random frames and \(\varSigma \varDelta \) quantization of compressed sensing measurements,
Primal and dual alternating direction algorithms for \(\ell _{1}\)-\(\ell _{1}\)-norm minimization problems in compressive sensing,
Thresholding-based iterative selection procedures for model selection and shrinkage,
The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso),
New nonsmooth equations-based algorithms for \(\ell_1\)-norm minimization and applications,
Reconstruction of nonuniformly sampled time-limited signals using prolate spheroidal wave functions,
A short note on compressed sensing with partially known signal support,
A preconditioning approach for improved estimation of sparse polynomial chaos expansions,
A simple and flexible model order reduction method for FFT-based homogenization problems using a sparse sampling technique,
A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness,
Minimization of transformed \(L_1\) penalty: theory, difference of convex function algorithm, and robust application in compressed sensing,
Dictionary evaluation and optimization for sparse coding based speech processing,
Debiasing the Lasso: optimal sample size for Gaussian designs,
Reconstructed error and linear representation coefficients restricted by \(\ell_1\)-minimization for face recognition under different illumination and occlusion,
Sparse signal inversion with impulsive noise by dual spectral projected gradient method,
Sparse approximation of fitting surface by elastic net,
Sparse polynomial interpolation: sparse recovery, super-resolution, or Prony?,
A performance guarantee for orthogonal matching pursuit using mutual coherence,
Sparse approximate reconstruction decomposed by two optimization problems,
Measurement matrix optimization via mutual coherence minimization for compressively sensed signals reconstruction,
Sparse principal component analysis via fractional function regularity,
A new linearized split Bregman iterative algorithm for image reconstruction in sparse-view X-ray computed tomography,
An algebraic perspective on integer sparse recovery,
Deterministic constructions of compressed sensing matrices based on optimal codebooks and codes,
Noisy Euclidean distance matrix completion with a single missing node,
Properties and iterative methods for the \(Q\)-lasso,
Compressed data separation via dual frames based split-analysis with Weibull matrices,
On recovery guarantees for one-bit compressed sensing on manifolds,
On the interplay between acceleration and identification for the proximal gradient algorithm,
Adaptive compressive learning for prediction of protein-protein interactions from primary sequence,
On the sparseness of 1-norm support vector machines,
Sparse harmonic transforms: a new class of sublinear-time algorithms for learning functions of many variables,
Sparse feedback design in discrete-time linear systems,
Customized dictionary learning for subdatasets with fine granularity,
Peeling decoding of LDPC codes with applications in compressed sensing,
A novel detection scheme with multiple observations for sparse signal based on likelihood ratio test with sparse estimation,
Norm-minimized scattering data from intensity spectra,
Augmented sparse reconstruction of protein signaling networks,
On the \(\ell^\infty\)-norms of the singular vectors of arbitrary powers of a difference matrix with applications to sigma-delta quantization,
Dual-density-based reweighted \(\ell_1\)-algorithms for a class of \(\ell_0\)-minimization problems,
Nonuniqueness of solutions of a class of \(\ell_0\)-minimization problems,
Memoryless scalar quantization for random frames,
Sparse approximate solutions to max-plus equations,
Inpainting via sparse recovery with directional constraints,
Structured iterative hard thresholding with on- and off-grid applications,
Localized Fourier analysis for graph signal processing,
Recovering sparse networks: basis adaptation and stability under extensions,
Robust sparse recovery via a novel convex model,
Ensemble Kalman inversion for sparse learning of dynamical systems from time-averaged data,
Learning ``best kernels from data in Gaussian process regression. With application to aerodynamics, Commonsense explanations of sparsity, Zipf law, and Nash's bargaining solution, On the robustness of minimum norm interpolators and regularized empirical risk minimizers, GenMod: a generative modeling approach for spectral representation of PDEs with random inputs, Sensitivity of low-rank matrix recovery, Increasing the semantic storage density of sparse distributed memory, Generalization bounds for sparse random feature expansions, Proof methods for robust low-rank matrix recovery, Atmospheric radar imaging improvements using compressed sensing and MIMO, Sparse recovery of sound fields using measurements from moving microphones, Adaptive iterative hard thresholding for least absolute deviation problems with sparsity constraints, A unified Douglas-Rachford algorithm for generalized DC programming, Weighted \(\ell_p\) (\(0<p\le 1\)) minimization with non-uniform weights for sparse recovery under partial support information, Sparse PSD approximation of the PSD cone, Sparse representation of vectors in lattices and semigroups, Convergence bounds for empirical nonlinear least-squares, Optimizing Sparsity over Lattices and Semigroups, WARPd: A Linearly Convergent First-Order Primal-Dual Algorithm for Inverse Problems with Approximate Sharpness Conditions, Sparse Approximations with Interior Point Methods, Sparse Representation for Sampled-Data $$H^\infty $$ Filters, Viscosity and inertial algorithms for the split common fixed point problem with applications to compressed sensing, A General Framework of Rotational Sparse Approximation in Uncertainty Quantification, Estimation of block sparsity in compressive sensing, On the Complexity Analysis of the Primal Solutions for the Accelerated Randomized Dual Coordinate Ascent, An Unbiased Approach to Low Rank Recovery, Sensor Placement Sensitivity and Robust Reconstruction of Wave Dynamics from Multiple Sensors, Deep Learning--Based Dictionary Learning and Tomographic Image Reconstruction, Convergence rates for the joint solution of inverse problems with compressed sensing data, Model Selection With Lasso-Zero: Adding Straw to the Haystack to Better Find Needles, A Compressed Sensing Framework for Monte Carlo Transport Simulations Using Random Disjoint Tallies, Minimization of $L_1$ Over $L_2$ for Sparse Signal Recovery with Convergence Guarantee, Exact reconstruction of extended exponential sums using rational approximation of their Fourier coefficients, Minimizing L 1 over L 2 norms on the gradient, Image Segmentation via Fischer-Burmeister Total Variation and Thresholding, Unnamed Item, An Accelerated Method for Derivative-Free Smooth Stochastic Convex Optimization, A Nonconvex Optimization Approach to IMRT Planning with Dose–Volume Constraints, $(L_r,L_r,1)$-Decompositions, Sparse Component Analysis, and the Blind Separation of Sums of Exponentials, Stable Image Reconstruction Using Transformed Total Variation Minimization, Performance analysis for unconstrained analysis based approaches*, Empirical Bayesian Inference Using a Support Informed Prior, Splines Are Universal Solutions of Linear Inverse Problems with Generalized TV Regularization, Point-process models of social network interactions: Parameter estimation and missing data recovery, Unnamed Item, A Generalization of Wirtinger Flow for Exact Interferometric Inversion, Weighted ${\ell}_{{1}}$-minimization for sparse recovery under arbitrary prior information, Representation and coding of signal geometry, Eight Great Reasons to Do Mathematics, Parameter Selection in Dynamic Contrast-Enhanced Magnetic Resonance Tomography, Joint reconstruction via coupled Bregman iterations with applications to PET-MR imaging, Sliced-Inverse-Regression--Aided Rotated Compressive Sensing Method for Uncertainty Quantification, An unbiased approach to compressed sensing, Stability analysis of a class of sparse optimization problems, Hierachical Bayesian models and sparsity: ℓ 2 -magic, Robust data-driven discovery of governing physical laws with error bars, Semiclassical Sampling and Discretization of Certain Linear Inverse Problems, Non-Negative Sparse Regression and Column Subset Selection with L1 Error, Higher-order total variation approaches and generalisations, Inferring Sparse Preference Lists from Partial Information, Sparsity Promoting Hybrid Solvers for Hierarchical Bayesian Inverse Problems, Leveraging Sparsity and Compressive Sensing for Reduced Order Modeling, Inexact primal–dual gradient projection methods for nonlinear optimization on convex set, Microlocal Analysis of the Geometric Separation Problem, Error Localization of Best $L_{1}$ Polynomial Approximants, Learning partial differential equations via data discovery and sparse optimization, Unnamed Item, A Gradient-Enhanced L1 Approach for the Recovery of Sparse Trigonometric Polynomials, Mathematics of Analog‐to‐Digital Conversion, Iteratively Reweighted Group Lasso Based on Log-Composite Regularization, Frames as Codes, The Trimmed Lasso: Sparse Recovery Guarantees and Practical Optimization by the Generalized Soft-Min Penalty, ELASTIC-NET REGULARIZATION FOR LOW-RANK MATRIX RECOVERY, Variance-stabilization-based compressive inversion under Poisson or Poisson–Gaussian noise with analytical bounds, Tight and full spark Chebyshev frames with real entries and worst-case coherence analysis, Optimal Compressive Imaging of Fourier Data, Spherical Designs and Nonconvex Minimization for Recovery of Sparse Signals on the Sphere, On Collaborative Compressive Sensing Systems: The Framework, Design, and Algorithm, A New Optimization Approach to Sparse Reconstruction of Log-Conductivity in Acousto-Electric Tomography, Compression and Conditional Emulation of Climate Model Output, A Scale-Invariant Approach for Sparse Signal Recovery, Unnamed Item, A Data-Independent Distance to Infeasibility for Linear Conic Systems, Subset Selection in Sparse Matrices, Welch bound-achieving compressed sensing matrices from optimal codebooks, Simple Classification using Binary Data, Frames for compressed sensing using coherence, Querying a Matrix Through Matrix-Vector Products., Sparse stabilization and control of alignment models, Mathematical methods in biomedical imaging, Stochastic Collocation Methods via Minimisation of the Transformed L<sub>1</sub>-Penalty, Compressed sensing-based reconstruction for computed tomography with translational trajectory, A Low-Rank Schwarz Method for Radiative Transfer Equation With Heterogeneous Scattering Coefficient, An optimal transport approach for solving dynamic inverse problems in spaces of measures, Analysis and Algorithms for Some Compressed Sensing Models Based on L1/L2 Minimization, Unnamed Item, ℓ 1 − αℓ 2 minimization methods for signal and image reconstruction with impulsive noise removal, Learning optical flow for fast MRI reconstruction, Unnamed Item, Unnamed Item, The Dantzig selector: recovery of signal via ℓ 1 − αℓ 2 minimization, Compressive Sensing-Based Computed Tomography Imaging: An effective approach for COVID-19 Detection, Data-Driven Reconstruction and Encoding of Sparse Stimuli across Convergent Sensory Layers from Downstream Neuronal Network Dynamics, Extended Dai-Yuan conjugate gradient strategy for large-scale unconstrained optimization with applications to compressive sensing, Decoding from Pooled Data: Sharp Information-Theoretic Bounds, Fast Convex Pruning of Deep Neural Networks, Combining line search and trust-region methods forℓ1-minimization, Low rank matrix recovery with adversarial sparse noise*