@poster{6256,
  title = {Solving large-scale nonnegative least-squares},
  journal = {16th Conference of the International Linear Algebra Society (ILAS 2010)},
  abstract = {We study the fundamental problem of nonnegative least
  squares. This problem was apparently introduced by Lawson
  and Hanson [1] under the name NNLS. As is evident
  from its name, NNLS seeks least-squares solutions that are
  also nonnegative. Owing to its wide-applicability numerous
  algorithms have been derived for NNLS, beginning from the
  active-set approach of Lawson and Hanson [1] leading up to
  the sophisticated interior-point method of Bellavia et al. [2].
  We present a new algorithm for NNLS that combines projected
  subgradients with the non-monotonic gradient descent
  idea of Barzilai and Borwein [3]. Our resulting algorithm is
  called BBSG, and we guarantee its convergence by exploiting
  properties of NNLS in conjunction with projected subgradients.
  BBSG is surprisingly simple and scales well to large
  problems. We substantiate our claims by empirically evaluating
  BBSG and comparing it with established convex solvers
  and specialized NNLS algorithms. The numerical results suggest
  that BBSG is a practical method for solving large-scale
  NNLS problems.},
  volume = {16},
  pages = {19},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jun,
  year = {2010},
  note = {based on Joint work with Dongmin Kim and Inderjit Dhillon},
  slug = {6256},
  author = {Sra, S.},
  month_numeric = {6}
}