Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Binary interaction methods for high dimensional global optimization and
  machine learning   In this work we introduce a new class of gradient-free global optimization
methods based on a binary interaction dynamics governed by a Boltzmann type
equation. In each interaction the particles act taking into account both the
best microscopic binary position and the best macroscopic collective position.
In the mean-field limit we show that the resulting Fokker-Planck partial
differential equations generalize the current class of consensus based
optimization (CBO) methods. For the latter methods, convergence to the global
minimizer can be shown for a large class of functions. Algorithmic
implementations inspired by the well-known direct simulation Monte Carlo
methods in kinetic theory are derived and discussed. Several examples on
prototype test functions for global optimization are reported including
applications to machine learning.

28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org