@techreport{5831,
  title = {Non-monotonic Poisson Likelihood Maximization},
  abstract = {This report summarizes the theory and some main applications of a new non-monotonic algorithm for
  maximizing a Poisson Likelihood, which for Positron Emission Tomography (PET) is equivalent to minimizing
  the associated Kullback-Leibler Divergence, and for Transmission Tomography is similar to maximizing the dual
  of a maximum entropy problem. We call our method non-monotonic maximum likelihood (NMML) and show
  its application to different problems such as tomography and image restoration. We discuss some theoretical
  properties such as convergence for our algorithm. Our experimental results indicate that speedups obtained via our
  non-monotonic methods are substantial.},
  number = {170},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max-Planck Institute for Biological Cybernetics, Tübingen, Germany},
  school = {Biologische Kybernetik},
  month = jun,
  year = {2008},
  author = {Sra, S. and Kim, D. and Sch{\"o}lkopf, B.},
  month_numeric = {6}
}