Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Constrained Signal Reconstruction by Randomized Epigraphical
  Projection   This paper proposes a randomized optimization framework for constrained
signal reconstruction, where the word "constrained" implies that data-fidelity
is imposed as a hard constraint instead of adding a data-fidelity term to an
objective function to be minimized. Such formulation facilitates the selection
of regularization terms and hyperparameters, but due to the non-separability of
the data-fidelity constraint, it does not suit block-coordinate-wise
randomization as is. To resolve this, we give another expression of the
data-fidelity constraint via epigraphs, which enables to design a randomized
solver based on a stochastic proximal algorithm with randomized epigraphical
projection. Our method is very efficient especially when the problem involves
non-structured large matrices. We apply our method to CT image reconstruction,
where the advantage of our method over the deterministic counterpart is
demonstrated.

30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg