Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributed Optimization via Adaptive Regularization for Large Problems
  with Separable Constraints   Many practical applications require solving an optimization over large and
high-dimensional data sets, which makes these problems hard to solve and
prohibitively time consuming. In this paper, we propose a parallel distributed
algorithm that uses an adaptive regularizer (PDAR) to solve a joint
optimization problem with separable constraints. The regularizer is adaptive
and depends on the step size between iterations and the iteration number. We
show theoretical converge of our algorithm to an optimal solution, and use a
multi-agent three-bin resource allocation example to illustrate the
effectiveness of the proposed algorithm. Numerical simulations show that our
algorithm converges to the same optimal solution as other distributed methods,
with significantly reduced computational time.

37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th