2 research outputs found
Proximal Langevin Algorithm: Rapid Convergence Under Isoperimetry
We study the Proximal Langevin Algorithm (PLA) for sampling from a
probability distribution on under isoperimetry.
We prove a convergence guarantee for PLA in Kullback-Leibler (KL) divergence
when satisfies log-Sobolev inequality (LSI) and has bounded second
and third derivatives. This improves on the result for the Unadjusted Langevin
Algorithm (ULA), and matches the fastest known rate for sampling under LSI
(without Metropolis filter) with a better dependence on the LSI constant. We
also prove convergence guarantees for PLA in R\'enyi divergence of order when the biased limit satisfies either LSI or Poincar\'e inequality
Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach. Part I: Methodology and Experiments
Many imaging problems require solving an inverse problem that is
ill-conditioned or ill-posed. Imaging methods typically address this difficulty
by regularising the estimation problem to make it well-posed. This often
requires setting the value of the so-called regularisation parameters that
control the amount of regularisation enforced. These parameters are notoriously
difficult to set a priori, and can have a dramatic impact on the recovered
estimates. In this work, we propose a general empirical Bayesian method for
setting regularisation parameters in imaging problems that are convex w.r.t.
the unknown image. Our method calibrates regularisation parameters directly
from the observed data by maximum marginal likelihood estimation, and can
simultaneously estimate multiple regularisation parameters. Furthermore, the
proposed algorithm uses the same basic operators as proximal optimisation
algorithms, namely gradient and proximal operators, and it is therefore
straightforward to apply to problems that are currently solved by using
proximal optimisation techniques. Our methodology is demonstrated with a range
of experiments and comparisons with alternative approaches from the literature.
The considered experiments include image denoising, non-blind image
deconvolution, and hyperspectral unmixing, using synthesis and analysis priors
involving the L1, total-variation, total-variation and L1, and
total-generalised-variation pseudo-norms. A detailed theoretical analysis of
the proposed method is presented in the companion paper arXiv:2008.05793.Comment: 37 pages - SIIMS 202