FaRSA for ℓ1-regularized convex optimization: local convergence and numerical experience

DOI10.1080/10556788.2017.1415336zbMath1390.49040OpenAlexW2794053546MaRDI QIDQ4638928

Tianyi Chen, Daniel P. Robinson, Frank E. Curtis

Publication date: 2 May 2018

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10556788.2017.1415336

zbMATH Keywords

convex optimizationnonlinear optimizationsparse optimizationactive-set methodssubspace minimizationmodel predictionreduced-space methods

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25) Nonlinear programming (90C30) Nonsmooth analysis (49J52) Numerical methods based on nonlinear programming (49M37)

Related Items

A Subspace Acceleration Method for Minimization Involving a Group Sparsity-Inducing Regularizer, On the stationarity for nonlinear optimization problems with polyhedral constraints, A subspace-accelerated split Bregman method for sparse data recovery with joint \(\ell_1\)-type regularizers

Uses Software

• Matlab
• LIBSVM
• FarRSA

Cites Work

• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• An inexact successive quadratic approximation method for L-1 regularized optimization
• A family of second-order methods for convex \(\ell _1\)-regularized optimization
• Practical inexact proximal quasi-Newton method with global complexity analysis
• A stabilized SQP method: superlinear convergence
• A second-order method for convex₁-regularized optimization with active-set prediction
• A New Active Set Algorithm for Box Constrained Optimization
• Inexact Newton Methods
• Sparse Reconstruction by Separable Approximation
• A stabilized SQP method: global convergence
• De-noising by soft-thresholding
• A Reduced-Space Algorithm for Minimizing $\ell_1$-Regularized Convex Functions
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