Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multigrid methods for tensor structured Markov chains with low rank
  approximation   Tensor structured Markov chains are part of stochastic models of many
practical applications, e.g., in the description of complex production or
telephone networks. The most interesting question in Markov chain models is the
determination of the stationary distribution as a description of the long term
behavior of the system. This involves the computation of the eigenvector
corresponding to the dominant eigenvalue or equivalently the solution of a
singular linear system of equations. Due to the tensor structure of the models
the dimension of the operators grows rapidly and a direct solution without
exploiting the tensor structure becomes infeasible. Algebraic multigrid methods
have proven to be efficient when dealing with Markov chains without using
tensor structure. In this work we present an approach to adapt the algebraic
multigrid framework to the tensor frame, not only using the tensor structure in
matrix-vector multiplications, but also tensor structured coarse-grid operators
and tensor representations of the solution vector.

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The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
1
The categories of this paper are: cs
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg