Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Linear Convergence of Primal-Dual Gradient Methods and their Performance
  in Distributed Optimization   In this work, we revisit a classical incremental implementation of the
primal-descent dual-ascent gradient method used for the solution of equality
constrained optimization problems. We provide a short proof that establishes
the linear (exponential) convergence of the algorithm for smooth
strongly-convex cost functions and study its relation to the non-incremental
implementation. We also study the effect of the augmented Lagrangian penalty
term on the performance of distributed optimization algorithms for the
minimization of aggregate cost functions over multi-agent networks.

0
The categories of this paper are: hep-ph
1
The categories of this paper are: cs
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom