Fast nonnegative least squares through flexible Krylov subspaces

Silvia Gazzola, Yves Wiaux

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)
79 Downloads (Pure)


Constrained linear least squares problems arise in a variety of applications, and many iterative methods are already available to compute their solutions. This paper proposes a new efficient approach to solve nonnegative linear least squares problems. The associated KKT conditions are leveraged to form an adaptively preconditioned least squares problem, which is then solved by a flexible and inexact Krylov subspace method. The new method can be easily applied to image reconstruction problems, where the components of the solution represent nonnegative intensities.
Numerical experiments and comparisons are displayed in order to validate the new method, which delivers results of equal or better quality than many state-of-the-art methods for nonnegative least squares solvers, with a significant speedup.
Original languageEnglish
Pages (from-to)A655–A679
Number of pages25
JournalSIAM Journal on Scientific Computing
Issue number2
Publication statusPublished - 27 Apr 2017


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