Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Approximating matrix eigenvalues by subspace iteration with repeated
  random sparsification   Traditional numerical methods for calculating matrix eigenvalues are
prohibitively expensive for high-dimensional problems. Iterative random
sparsification methods allow for the estimation of a single dominant eigenvalue
at reduced cost by leveraging repeated random sampling and averaging. We
present a general approach to extending such methods for the estimation of
multiple eigenvalues and demonstrate its performance for several benchmark
problems in quantum chemistry.

21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th