Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Steady-state Simulation of High-dimensional Stochastic
  Networks   We propose and study an asymptotically optimal Monte Carlo estimator for
steady-state expectations of a d-dimensional reflected Brownian motion. Our
estimator is asymptotically optimal in the sense that it requires
$\tilde{O}(d)$ (up to logarithmic factors in $d$) i.i.d. Gaussian random
variables in order to output an estimate with a controlled error. Our
construction is based on the analysis of a suitable multi-level Monte Carlo
strategy which, we believe, can be applied widely. This is the first algorithm
with linear complexity (under suitable regularity conditions) for steady-state
estimation of RBM as the dimension increases.

23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph