Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Blind deconvolution of covariance matrix inverses for autoregressive
  processes   Matrix $\mathbf{C}$ can be blindly deconvoluted if there exist matrices
$\mathbf{A}$ and $\mathbf{B}$ such that $\mathbf{C}= \mathbf{A} \ast
\mathbf{B}$, where $\ast$ denotes the operation of matrix convolution. We study
the problem of matrix deconvolution in the case where matrix $\mathbf{C}$ is
proportional to the inverse of the autocovariance matrix of an autoregressive
process. We show that the deconvolution of such matrices is important in
problems of Hankel structured low-rank approximation (HSLRA). In the cases of
autoregressive models of orders one and two, we fully characterize the range of
parameters where such deconvolution can be performed and provide construction
schemes for performing deconvolutions. We also consider general autoregressive
models of order $p$, where we prove that the deconvolution $\mathbf{C}=
\mathbf{A} \ast \mathbf{B}$ does not exist if the matrix $\mathbf{B}$ is
diagonal and its size is larger than $p$.

21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat