Fast global convergence of gradient methods for high-dimensional statistical recovery
type: Item
instance of: scholarly article
title: Fast global convergence of gradient methods for high-dimensional statistical recovery
zbMATH Open document ID: 1373.62244
DOI: 10.1214/12-AOS1032
author: Alekh Agarwal
author: Martin J. Wainwright
author: Sahand Negahban
published in: The Annals of Statistics
publication date: 2014-09-15T00:00:00Z
full work available at URL: https://arxiv.org/abs/1104.4824
full work available at URL: https://projecteuclid.org/euclid.aos/1359987527
Mathematics Subject Classification ID: 62H12
Mathematics Subject Classification ID: 62F30
Mathematics Subject Classification ID: 62J07
Mathematics Subject Classification ID: 90C25
zbMATH DE Number: 6344382
zbMATH Keywords: high-dimensional inference
zbMATH Keywords: convex optimization
zbMATH Keywords: regularized \(M\)-estimation
describes a project that uses: PDCO
describes a project that uses: NESTA
MaRDI profile type: Publication
arXiv ID: 1104.4824
cites work: Noisy matrix decomposition via convex relaxation: optimal rates in high dimensions
cites work: High-dimensional analysis of semidefinite relaxations for sparse principal components
cites work: A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
cites work: NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
cites work: Q4209222
cites work: Simultaneous analysis of Lasso and Dantzig selector
cites work: Q4821526
cites work: Linear convergence of iterative soft-thresholding
cites work: Robust principal component analysis?
cites work: Exact matrix completion via convex optimization
cites work: Rank-Sparsity Incoherence for Matrix Decomposition
cites work: Atomic Decomposition by Basis Pursuit
cites work: Fixed-Point Continuation for $\ell_1$-Minimization: Methodology and Convergence
cites work: Robust Matrix Decomposition With Sparse Corruptions
cites work: The benefit of group sparsity
cites work: Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion
cites work: High-dimensional regression with noisy and missing data: provable guarantees with nonconvexity
cites work: Error bounds and convergence analysis of feasible descent methods: A general approach
cites work: High-dimensional graphs and variable selection with the Lasso
cites work: A unified framework for high-dimensional analysis of \(M\)-estimators with decomposable regularizers
cites work: Estimation of (near) low-rank matrices with noise and high-dimensional scaling
cites work: Restricted strong convexity and weighted matrix completion: Optimal bounds with noise
cites work: Introductory lectures on convex optimization. A basic course.
cites work: Paraconvex functions and paraconvex sets
cites work: Restricted eigenvalue properties for correlated Gaussian designs
cites work: Minimax Rates of Estimation for High-Dimensional Linear Regression Over $\ell_q$-Balls
cites work: A Simpler Approach to Matrix Completion
cites work: Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
cites work: Estimation of high-dimensional low-rank matrices
cites work: Reconstruction From Anisotropic Random Measurements
cites work: Q4864293
cites work: Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
cites work: On the conditions used to prove oracle results for the Lasso
cites work: Robust PCA via Outlier Pursuit
cites work: The sparsity and bias of the LASSO selection in high-dimensional linear regression
cites work: The composite absolute penalties family for grouped and hierarchical variable selection
OpenAlex ID: W2566240941
description: scientific article