Fast Nonnegative Least Squares Through Flexible Krylov Subspaces

DOI10.1137/15M1048872zbMath1365.65161arXiv1511.06269OpenAlexW2962832489MaRDI QIDQ5738172

Y. Wiaux, Silvia Gazzola

Publication date: 31 May 2017

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

Full work available at URL: https://arxiv.org/abs/1511.06269

zbMATH Keywords

Gaussian noisenumerical experimentimage reconstructionKarush-Kuhn-Tucker conditionsconjugate gradientnonnegative least squaresPoisson noiseflexible Krylov methodsnonnegative linear least squares problems

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Quadratic programming (90C20) Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (9)

A computational framework for edge-preserving regularization in dynamic inverse problems ⋮ IR tools: a MATLAB package of iterative regularization methods and large-scale test problems ⋮ Image regularization by nonnegatively constrained conjugate gradient ⋮ Krylov methods for inverse problems: Surveying classical, and introducing new, algorithmic approaches ⋮ Bounded perturbations resilient iterative methods for linear systems and least squares problems: operator-based approaches, analysis, and performance evaluation ⋮ Column-oriented algebraic iterative methods for nonnegative constrained least squares problems ⋮ Mathematical modeling for 2D light-sheet fluorescence microscopy image reconstruction ⋮ Linearized Krylov subspace Bregman iteration with nonnegativity constraint ⋮ Flexible Krylov Methods for $\ell_p$ Regularization

Uses Software

• AIR tools

Cites Work

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