Interior-point gradient method for large-scale totally nonnegative least squares problems

DOI10.1007/s10957-005-2668-zzbMath1093.90084OpenAlexW2082445317MaRDI QIDQ813389

Yin Zhang, Michael Merritt

Publication date: 8 February 2006

Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/1911/102020

zbMATH Keywords

interior-point gradient methodsTotally nonnegative least-squares problems

Mathematics Subject Classification ID

Quadratic programming (90C20) Interior-point methods (90C51)

Related Items (12)

An adaptive accelerated first-order method for convex optimization ⋮ Descent Methods for Nonnegative Matrix Factorization ⋮ A fast eigenvalue approach for solving the trust region subproblem with an additional linear inequality ⋮ Bi-level algorithm for optimizing hyperparameters in penalized nonnegative matrix factorization ⋮ An Alternating Rank-k Nonnegative Least Squares Framework (ARkNLS) for Nonnegative Matrix Factorization ⋮ Dose-volume-based IMRT fluence optimization: a fast least-squares approach with differentia\-bility ⋮ Sparse solution of nonnegative least squares problems with applications in the construction of probabilistic Boolean networks ⋮ An alternating projected gradient algorithm for nonnegative matrix factorization ⋮ Alternative gradient algorithms with applications to nonnegative matrix factorizations ⋮ Algorithms for nonnegative matrix and tensor factorizations: a unified view based on block coordinate descent framework ⋮ Fast Projection‐Based Methods for the Least Squares Nonnegative Matrix Approximation Problem ⋮ A Scaled Gradient Projection Method for Bayesian Learning in Dynamical Systems

Cites Work

• Convex programming in Hilbert space

This page was built for publication: Interior-point gradient method for large-scale totally nonnegative least squares problems