Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Stochastic Trust Region Algorithm Based on Careful Step Normalization   An algorithm is proposed for solving stochastic and finite sum minimization
problems. Based on a trust region methodology, the algorithm employs normalized
steps, at least as long as the norms of the stochastic gradient estimates are
within a specified interval. The complete algorithm---which dynamically chooses
whether or not to employ normalized steps---is proved to have convergence
guarantees that are similar to those possessed by a traditional stochastic
gradient approach under various sets of conditions related to the accuracy of
the stochastic gradient estimates and choice of stepsize sequence. The results
of numerical experiments are presented when the method is employed to minimize
convex and nonconvex machine learning test problems. These results illustrate
that the method can outperform a traditional stochastic gradient approach.

8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
1
The categories of this paper are: cs