Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Gradient Estimation with Simultaneous Perturbation and Compressive
  Sensing   This paper aims at achieving a "good" estimator for the gradient of a
function on a high-dimensional space. Often such functions are not sensitive in
all coordinates and the gradient of the function is almost sparse. We propose a
method for gradient estimation that combines ideas from Spall's Simultaneous
Perturbation Stochastic Approximation with compressive sensing. The aim is to
obtain "good" estimator without too many function evaluations. Application to
estimating gradient outer product matrix as well as standard optimization
problems are illustrated via simulations.

7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
19
The categories of this paper are: eess
1
The categories of this paper are: cs
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int