Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Computationally Efficient Limited Memory CMA-ES for Large Scale
  Optimization   We propose a computationally efficient limited memory Covariance Matrix
Adaptation Evolution Strategy for large scale optimization, which we call the
LM-CMA-ES. The LM-CMA-ES is a stochastic, derivative-free algorithm for
numerical optimization of non-linear, non-convex optimization problems in
continuous domain. Inspired by the limited memory BFGS method of Liu and
Nocedal (1989), the LM-CMA-ES samples candidate solutions according to a
covariance matrix reproduced from $m$ direction vectors selected during the
optimization process. The decomposition of the covariance matrix into Cholesky
factors allows to reduce the time and memory complexity of the sampling to
$O(mn)$, where $n$ is the number of decision variables. When $n$ is large
(e.g., $n$ > 1000), even relatively small values of $m$ (e.g., $m=20,30$) are
sufficient to efficiently solve fully non-separable problems and to reduce the
overall run-time.

24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math