Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Limited benefit of cooperation in distributed relative localization   Important applications in robotic and sensor networks require distributed
algorithms to solve the so-called relative localization problem: a node-indexed
vector has to be reconstructed from measurements of differences between
neighbor nodes. In a recent note, we have studied the estimation error of a
popular gradient descent algorithm showing that the mean square error has a
minimum at a finite time, after which the performance worsens. This paper
proposes a suitable modification of this algorithm incorporating more realistic
"a priori" information on the position. The new algorithm presents a
performance monotonically decreasing to the optimal one. Furthermore, we show
that the optimal performance is approximated, up to a 1 + \eps factor, within a
time which is independent of the graph and of the number of nodes. This
convergence time is very much related to the minimum exhibited by the previous
algorithm and both lead to the following conclusion: in the presence of noisy
data, cooperation is only useful till a certain limit.

20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga