Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On entropic convergence of algorithms in terms of domain partitions   The paper describes an approach to measuring convergence of an algorithm to
its result in terms of an entropy-like function of partitions of its inputs of
a given length. The goal is to look at the algorithmic data processing from the
viewpoint of information transformation, with a hope to better understand the
work of algorithm, and maybe its complexity. The entropy is a measure of
uncertainty, it does not correspond to our intuitive understanding of
information. However, it is what we have in this area. In order to realize this
approach we introduce a measure on the inputs of a given length based on the
Principle of Maximal Uncertainty: all results should be equiprobable to the
algorithm at the beginning. An algorithm is viewed as a set of events, each
event is an application of a command. The commands are very basic. To measure
the convergence we introduce a measure that is called entropic weight of events
of the algorithm. The approach is illustrated by two examples.

15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
16
The categories of this paper are: stat
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th