Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tensors in Power System Computation I: Distributed Computation for
  Optimal Power Flow, DC OPF   Tensor decomposition plays a key role in identifying common features across a
collection of matrices in many areas of science. A fundamental need in big data
research is to process data tabulated as large-scale matrices using
eigenvectors. A higher order generalized singular value decomposition technique
successfully captures the common features of the same organ from multiple
animals in genomic signal processing. A recent semidefinite programming
approach to solve an AC optimal power flow was accompanied by the problem
formulation in the Cartesian coordinate system. The collection of nodal
Kirchhoff laws introduces a 3D tensor with a common feature of individual
matrices to maintain local power balance. In this paper, the mathematical
process is established and the common feature is identified. The common feature
is a key element to a fully decentralized and therefore scalable algorithm to
solve AC optimal power flow.

24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex