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Equivalent Lipschitz surrogates for zero-norm and rank optimization problems

https://doi.org/10.1007/s10898-018-0675-5
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Optimality conditions for bilevel programming problems
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Rank reduction of correlation matrices by majorization
inferredcites →https://doi.org/10.1109/acc.2003.1243393openalex-snapshot
Log-det heuristic for matrix rank minimization with applications to Hankel and Euclidean distance matrices
inferredcites →https://doi.org/10.1109/icassp.2012.6288484openalex-snapshot
Hyperspectral image compressed sensing via low-rank and joint-sparse matrix recovery
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Stable Principal Component Pursuit
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Matrix Completion From a Few Entries
inferredcites →https://doi.org/10.1109/tit.2011.2111771openalex-snapshot
Tight Oracle Inequalities for Low-Rank Matrix Recovery From a Minimal Number of Noisy Random Measurements
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Simultaneously Structured Models With Application to Sparse and Low-Rank Matrices
inferredcites →https://doi.org/10.1109/tpami.2011.282openalex-snapshot
RASL: Robust Alignment by Sparse and Low-Rank Decomposition for Linearly Correlated Images
inferredcites →https://doi.org/10.1111/j.1467-9868.2005.00532.xopenalex-snapshot
Model Selection and Estimation in Regression with Grouped Variables
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Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
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Improved Iteratively Reweighted Least Squares for Unconstrained Smoothed $\ell_q$ Minimization
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Exact Penalty Decomposition Method for Zero-Norm Minimization Based on MPEC Formulation
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Penalty Methods for a Class of Non-Lipschitz Optimization Problems
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Multistage Convex Relaxation Approach to Rank Regularized Minimization Problems Based on Equivalent Mathematical Program with a Generalized Complementarity Constraint
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The Adaptive Lasso and Its Oracle Properties
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Estimation of (near) low-rank matrices with noise and high-dimensional scaling
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Necessary Optimality Conditions for Optimization Problems with Variational Inequality Constraints
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Convex Analysis
inferredcites →https://doi.org/10.48550/arxiv.0707.3390openalex-snapshot
Consistency of the group Lasso and multiple kernel learning
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Methods for Sparse and Low-Rank Recovery under Simplex Constraints
inferredcites →https://doi.org/10.68381/jca02012openalex-snapshot
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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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An Accelerated Semi-Proximal ADMM with Applications to Multi-Block Sparse Optimization Problems
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Second-order Optimality Conditions for Mathematical Program with Semidefinite Cone Complementarity Constraints and Applications
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Estimation of l0 norm penalized models: A statistical treatment
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A relaxation method for binary orthogonal optimization problems based on manifold gradient method and its applications
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Local optimality for stationary points of group zero-norm regularized problems and equivalent surrogates
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Twice epi-differentiablity and parabolic regularity of a class of non-amenable functions
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On $\ell_{0}$ Bregman-Relaxations for Kullback-Leibler Sparse Regression
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Several Classes of Stationary Points for Rank Regularized Minimization Problems
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Group Sparse Optimization for Images Recovery Using Capped Folded Concave Functions
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Robust Tensor Completion: Equivalent Surrogates, Error Bounds, and Algorithms
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New ℓ2 − ℓ0 algorithm for single-molecule localization microscopy
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A Relaxation Method for Binary Orthogonal Optimization Problems with its Applications
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Second-order optimality conditions for SDCMPCC and application to rank optimization problems
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