Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Proximal-Proximal-Gradient Method   In this paper, we present the proximal-proximal-gradient method (PPG), a
novel optimization method that is simple to implement and simple to
parallelize. PPG generalizes the proximal-gradient method and ADMM and is
applicable to minimization problems written as a sum of many differentiable and
many non-differentiable convex functions. The non-differentiable functions can
be coupled. We furthermore present a related stochastic variation, which we
call stochastic PPG (S-PPG). S-PPG can be interpreted as a generalization of
Finito and MISO over to the sum of many coupled non-differentiable convex
functions. We present many applications that can benefit from PPG and S-PPG and
prove convergence for both methods. A key strength of PPG and S-PPG is,
compared to existing methods, its ability to directly handle a large sum of
non-differentiable non-separable functions with a constant stepsize independent
of the number of functions. Such non-diminishing stepsizes allows them to be
fast.

14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
1
The categories of this paper are: cs
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
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The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an