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DC Approximation Approach for ℓ0-minimization in Compressed Sensing (Q2808067)

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Language	Label	Description	Also known as
English	DC Approximation Approach for ℓ0-minimization in Compressed Sensing	scientific article

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scholarly article

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DC Approximation Approach for ℓ0-minimization in Compressed Sensing (English)

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Nguyên Thị Bích Thủy

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Advanced Computational Methods for Knowledge Engineering

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publication date

26 May 2016

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zbMATH Keywords

compressed sensing

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sparse recovery

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\(\ell_0\)-norm

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DC programming

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DCA

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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/10.1007/978-3-319-17996-4_4

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Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality

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Atomic Decomposition by Basis Pursuit

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Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?

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Enhancing sparsity by reweighted \(\ell _{1}\) minimization

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Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information

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Highly Robust Error Correction byConvex Programming

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Iteratively reweighted least squares minimization for sparse recovery

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Compressed sensing

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Uncertainty principles and ideal atomic decomposition

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Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties

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Sparsest solutions of underdetermined linear systems via \( \ell _q\)-minimization for \(0<q\leqslant 1\)

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Recovering Sparse Signals With a Certain Family of Nonconvex Penalties and DC Programming

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Fast Sparse Representation Based on Smoothed ℓ0 Norm

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Improved Iteratively Reweighted Least Squares for Unconstrained Smoothed $\ell_q$ Minimization

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Convex analysis approach to d. c. programming: Theory, algorithms and applications

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A D.C. Optimization Algorithm for Solving the Trust-Region Subproblem

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The DC (Difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems

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A DC programming approach for feature selection in support vector machines learning

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DC approximation approaches for sparse optimization

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A DC Programming Approach for Finding Communities in Networks

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Feature selection for linear SVMs under uncertain data: robust optimization based on difference of convex functions algorithms

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Learning sparse classifiers with difference of convex functions algorithms

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A bilinear formulation for vector sparsity optimization

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Concave programming for finding sparse solutions to problems with convex constraints

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Concave programming for minimizing the zero-norm over polyhedral sets

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An affine scaling methodology for best basis selection

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DC Programming Approach for a Class of Nonconvex Programs Involving l 0 Norm

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

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Reweighted $\ell_1$-Minimization for Sparse Solutions to Underdetermined Linear Systems

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The Adaptive Lasso and Its Oracle Properties

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One-step sparse estimates in nonconcave penalized likelihood models

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Identifiers

zbMATH Open document ID

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10.1007/978-3-319-17996-4_4

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Mathematics Subject Classification ID

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zbMATH DE Number

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Sitelinks

Mathematics(1 entry)

mardi Publication:2808067

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