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A simple homotopy proximal mapping algorithm for compressive sensing (Q2425244)

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Language	Label	Description	Also known as
English	A simple homotopy proximal mapping algorithm for compressive sensing	scientific article

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scholarly article

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A simple homotopy proximal mapping algorithm for compressive sensing (English)

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Machine Learning

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publication date

26 June 2019

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zbMATH Keywords

compressive sensing

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sparse signal recovery

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proximal mapping

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linear convergence

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describes a project that uses

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MaRDI profile type

MaRDI publication profile

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full work available at URL

https://doi.org/10.1007/s10994-018-5772-7

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Fast global convergence of gradient methods for high-dimensional statistical recovery

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Sparse Recovery of Streaming Signals Using <formula formulatype="inline"><tex Notation="TeX">$\ell_1$</tex></formula>-Homotopy

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NESTA: A Fast and Accurate First-Order Method for Sparse Recovery

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A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems

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Simultaneous analysis of Lasso and Dantzig selector

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Iterative hard thresholding for compressed sensing

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A primal-dual homotopy algorithm for \(\ell _{1}\)-minimization with \(\ell _{\infty }\)-constraints

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Linear convergence of iterative soft-thresholding

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Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-Rank Matrices

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The restricted isometry property and its implications for compressed sensing

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Stable signal recovery from incomplete and inaccurate measurements

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Decoding by Linear Programming

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The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).

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Atomic Decomposition by Basis Pursuit

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Atomic Decomposition by Basis Pursuit

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A sparse Johnson

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Adaptive greedy approximations

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Compressed sensing

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Observed universality of phase transitions in high-dimensional geometry, with implications for modern data analysis and signal processing

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Accurate Prediction of Phase Transitions in Compressed Sensing via a Connection to Minimax Denoising

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The Noise-Sensitivity Phase Transition in Compressed Sensing

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Fast Solution of $\ell _{1}$-Norm Minimization Problems When the Solution May Be Sparse

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Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit

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Least angle regression. (With discussion)

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Decomposable norm minimization with proximal-gradient homotopy algorithm

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Hard Thresholding Pursuit: An Algorithm for Compressive Sensing

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Bonferroni inequalities

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Fixed-Point Continuation for $\ell_1$-Minimization: Methodology and Convergence

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A Bound on Tail Probabilities for Quadratic Forms in Independent Random Variables

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Extensions of Lipschitz mappings into a Hilbert space

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Sparser Johnson-Lindenstrauss Transforms

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Oracle inequalities in empirical risk minimization and sparse recovery problems. École d'Été de Probabilités de Saint-Flour XXXVIII-2008.

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Matrix recipes for hard thresholding methods

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An adaptive accelerated proximal gradient method and its homotopy continuation for sparse optimization

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An infeasible-point subgradient method using adaptive approximate projections

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Solving Basis Pursuit

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Matching pursuits with time-frequency dictionaries

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High-dimensional graphs and variable selection with the Lasso

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CoSaMP: Iterative signal recovery from incomplete and inaccurate samples

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Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit

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A new approach to variable selection in least squares problems

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Sharp Time–Data Tradeoffs for Linear Inverse Problems

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One-Bit Compressed Sensing by Linear Programming

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Convex Analysis

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The Computational Complexity of the Restricted Isometry Property, the Nullspace Property, and Related Concepts in Compressed Sensing

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Greed is Good: Algorithmic Results for Sparse Approximation

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Just relax: convex programming methods for identifying sparse signals in noise

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Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit

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On the conditions used to prove oracle results for the Lasso

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Probing the Pareto Frontier for Basis Pursuit Solutions

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Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)

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A Fast Algorithm for Sparse Reconstruction Based on Shrinkage, Subspace Optimization, and Continuation

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Sparse Reconstruction by Separable Approximation

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A Proximal-Gradient Homotopy Method for the Sparse Least-Squares Problem

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Some sharp performance bounds for least squares regression with \(L_1\) regularization

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The sparsity and bias of the LASSO selection in high-dimensional linear regression

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Identifiers

zbMATH Open document ID

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Mathematics Subject Classification ID

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zbMATH DE Number

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10.1007/S10994-018-5772-7

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Sitelinks

Mathematics(1 entry)

mardi Publication:2425244

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