Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stochastic Proximal Gradient Algorithms for Multi-Source Quantitative
  Photoacoustic Tomography   The development of accurate and efficient image reconstruction algorithms is
a central aspect of quantitative photoacoustic tomography (QPAT). In this
paper, we address this issues for multi-source QPAT using the radiative
transfer equation (RTE) as accurate model for light transport. The tissue
parameters are jointly reconstructed from the acoustical data measured for each
of the applied sources. We develop stochastic proximal gradient methods for
multi-source QPAT, which are more efficient than standard proximal gradient
methods in which a single iterative update has complexity proportional to the
number applies sources. Additionally, we introduce a completely new formulation
of QPAT as multilinear (MULL) inverse problem which avoids explicitly solving
the RTE. The MULL formulation of QPAT is again addressed with stochastic
proximal gradient methods. Numerical results for both approaches are presented.
Besides the introduction of stochastic proximal gradient algorithms to QPAT, we
consider the new MULL formulation of QPAT as main contribution of this paper.

18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an