Consistency bounds and support recovery of d-stationary solutions of sparse sample average approximations
type: Item
instance of: scholarly article
title: Consistency bounds and support recovery of d-stationary solutions of sparse sample average approximations
zbMATH Open document ID: 1465.90067
author: Miju Ahn
published in: Journal of Global Optimization
publication date: 2021-04-28T00:00:00Z
Mathematics Subject Classification ID: 90C26
zbMATH DE Number: 7340604
zbMATH Keywords: non-convex optimization
zbMATH Keywords: sparse learning
zbMATH Keywords: difference-of-convex program
zbMATH Keywords: directional stationary solution
MaRDI profile type: Publication
full work available at URL: s10898-019-00857-z
OpenAlex ID: W2983611585
Wikidata QID: Q126801873
cites work: Difference-of-Convex Learning: Directional Stationarity, Optimality, and Sparsity
cites work: Q3151174
cites work: Statistics for high-dimensional data. Methods, theory and applications.
cites work: Simultaneous analysis of Lasso and Dantzig selector
cites work: Decoding by Linear Programming
cites work: Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
cites work: The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
cites work: Enhancing sparsity by reweighted \(\ell _{1}\) minimization
cites work: Structural properties of affine sparsity constraints
cites work: Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
cites work: Nonconcave Penalized Likelihood With NP-Dimensionality
cites work: Q5251797
cites work: Asymptotics for Lasso-type estimators.
cites work: The DC (Difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems
cites work: DC approximation approaches for sparse optimization
cites work: Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima
cites work: Support recovery without incoherence: a case for nonconvex regularization
cites work: Point source super-resolution via non-convex \(L_1\) based methods
cites work: Confidence Intervals and Regions for the Lasso by Using Stochastic Variational Inequality Techniques in Optimization
cites work: A unified framework for high-dimensional analysis of \(M\)-estimators with decomposable regularizers
cites work: Local Strong Homogeneity of a Regularized Estimator
cites work: Computing B-Stationary Points of Nonsmooth DC Programs
cites work: Decomposition Methods for Computing Directional Stationary Solutions of a Class of Nonsmooth Nonconvex Optimization Problems
cites work: Lectures on Stochastic Programming
cites work: Q4864293
cites work: Minimization of $\ell_{1-2}$ for Compressed Sensing
cites work: Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
cites work: Nearly unbiased variable selection under minimax concave penalty
cites work: Minimization of transformed \(L_1\) penalty: theory, difference of convex function algorithm, and robust application in compressed sensing
cites work: Q3174050
cites work: The Adaptive Lasso and Its Oracle Properties
DOI: 10.1007/S10898-019-00857-Z
description: scientific article; zbMATH DE number 7340604