An attention algorithm for solving large scale structured \(l_0\)-norm penalty estimation problems

DOI10.1007/s42081-020-00101-zzbMath1477.62017OpenAlexW3120742959WikidataQ114687074 ScholiaQ114687074MaRDI QIDQ825333

Yu-Min Yen, Tso-Jung Yen

Publication date: 17 December 2021

Published in: Japanese Journal of Statistics and Data Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s42081-020-00101-z

zbMATH Keywords

model selectionnonconvex optimizationrandomized algorithmsproximal operatorsblockwise coordinate descent algorithms

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Linear regression; mixed models (62J05) Numerical mathematical programming methods (65K05)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• Parallel coordinate descent methods for big data optimization
• Best subset selection via a modern optimization lens
• Proximal alternating linearized minimization for nonconvex and nonsmooth problems
• A majorization-minimization approach to variable selection using spike and slab priors
• Incremental proximal methods for large scale convex optimization
• Iterative hard thresholding for compressed sensing
• Introductory lectures on convex optimization. A basic course.
• Structured variable selection via prior-induced hierarchical penalty functions
• Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward-backward splitting, and regularized Gauss-Seidel methods
• Coordinate descent with arbitrary sampling I: algorithms and complexity^†
• Proximal Splitting Methods in Signal Processing
• Efficiency of Coordinate Descent Methods on Huge-Scale Optimization Problems
• Accelerated, Parallel, and Proximal Coordinate Descent
• Characterizations of Łojasiewicz inequalities: Subgradient flows, talweg, convexity
• Robust Stochastic Approximation Approach to Stochastic Programming
• Sparse Reconstruction by Separable Approximation
• Adaptive Randomized Coordinate Descent for Sparse Systems: Lasso and Greedy Algorithms
• Distributed optimization with arbitrary local solvers
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Conditional Gradient Algorithmsfor Rank-One Matrix Approximations with a Sparsity Constraint
• On the Convergence of Block Coordinate Descent Type Methods
• The Łojasiewicz Inequality for Nonsmooth Subanalytic Functions with Applications to Subgradient Dynamical Systems
• Strong Rules for Discarding Predictors in Lasso-Type Problems
• A Stochastic Approximation Method
• Convex analysis and monotone operator theory in Hilbert spaces
• Convergence of a block coordinate descent method for nondifferentiable minimization
• Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm

This page was built for publication: An attention algorithm for solving large scale structured \(l_0\)-norm penalty estimation problems