Adapting Regularized Low-Rank Models for Parallel Architectures

DOI10.1137/17M1147342zbMath1405.65079arXiv1702.02241OpenAlexW2964189626MaRDI QIDQ4646456

Stephen R. Becker, Aleksandr Y. Aravkin, Derek Driggs

Publication date: 14 January 2019

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1702.02241

zbMATH Keywords

optimizationparallel computingrobust PCAGPU computationlow-rank models

Mathematics Subject Classification ID

Related Items (3)

Estimating shape parameters of piecewise linear-quadratic problems ⋮ Smooth over-parameterized solvers for non-smooth structured optimization ⋮ ADMM for multiaffine constrained optimization

Uses Software

• LMaFit
• minFunc
• LowRankModels
• Pyglrm

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
• Nonsmooth optimization via quasi-Newton methods
• SLOPE-adaptive variable selection via convex optimization
• Towards dense linear algebra for hybrid GPU accelerated manycore systems
• Level-set methods for convex optimization
• Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm
• Nonsmoothness and a variable metric method
• Local minima and convergence in low-rank semidefinite programming
• Generalized Low Rank Models
• Scalable Robust Matrix Recovery: Frank--Wolfe Meets Proximal Methods
• Communication-optimal Parallel and Sequential QR and LU Factorizations
• Optimization with Sparsity-Inducing Penalties
• Fast Methods for Denoising Matrix Completion Formulations, with Applications to Robust Seismic Data Interpolation
• Robust principal component analysis?
• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• Local Regression and Likelihood
• Variational Analysis
• Nonsmooth Variants of Powell's BFGS Convergence Theorem
• Sparse Approximate Solutions to Linear Systems
• Regularization and Variable Selection Via the Elastic Net
• Low-rank matrix completion using alternating minimization
• Augmented Lagrangian alternating direction method for matrix separation based on low-rank factorization
• Convex analysis and monotone operator theory in Hilbert spaces

This page was built for publication: Adapting Regularized Low-Rank Models for Parallel Architectures