10,103 research outputs found
Algorithmic information and incompressibility of families of multidimensional networks
This article presents a theoretical investigation of string-based generalized
representations of families of finite networks in a multidimensional space.
First, we study the recursive labeling of networks with (finite) arbitrary node
dimensions (or aspects), such as time instants or layers. In particular, we
study these networks that are formalized in the form of multiaspect graphs. We
show that, unlike classical graphs, the algorithmic information of a
multidimensional network is not in general dominated by the algorithmic
information of the binary sequence that determines the presence or absence of
edges. This universal algorithmic approach sets limitations and conditions for
irreducible information content analysis in comparing networks with a large
number of dimensions, such as multilayer networks. Nevertheless, we show that
there are particular cases of infinite nesting families of finite
multidimensional networks with a unified recursive labeling such that each
member of these families is incompressible. From these results, we study
network topological properties and equivalences in irreducible information
content of multidimensional networks in comparison to their isomorphic
classical graph.Comment: Extended preprint version of the pape
A toolbox to solve coupled systems of differential and difference equations
We present algorithms to solve coupled systems of linear differential
equations, arising in the calculation of massive Feynman diagrams with local
operator insertions at 3-loop order, which do {\it not} request special choices
of bases. Here we assume that the desired solution has a power series
representation and we seek for the coefficients in closed form. In particular,
if the coefficients depend on a small parameter \ep (the dimensional
parameter), we assume that the coefficients themselves can be expanded in
formal Laurent series w.r.t.\ \ep and we try to compute the first terms in
closed form. More precisely, we have a decision algorithm which solves the
following problem: if the terms can be represented by an indefinite nested
hypergeometric sum expression (covering as special cases the harmonic sums,
cyclotomic sums, generalized harmonic sums or nested binomial sums), then we
can calculate them. If the algorithm fails, we obtain a proof that the terms
cannot be represented by the class of indefinite nested hypergeometric sum
expressions. Internally, this problem is reduced by holonomic closure
properties to solving a coupled system of linear difference equations. The
underlying method in this setting relies on decoupling algorithms, difference
ring algorithms and recurrence solving. We demonstrate by a concrete example
how this algorithm can be applied with the new Mathematica package
\texttt{SolveCoupledSystem} which is based on the packages \texttt{Sigma},
\texttt{HarmonicSums} and \texttt{OreSys}. In all applications the
representation in -space is obtained as an iterated integral representation
over general alphabets, generalizing Poincar\'{e} iterated integrals
The Thermodynamics of Network Coding, and an Algorithmic Refinement of the Principle of Maximum Entropy
The principle of maximum entropy (Maxent) is often used to obtain prior
probability distributions as a method to obtain a Gibbs measure under some
restriction giving the probability that a system will be in a certain state
compared to the rest of the elements in the distribution. Because classical
entropy-based Maxent collapses cases confounding all distinct degrees of
randomness and pseudo-randomness, here we take into consideration the
generative mechanism of the systems considered in the ensemble to separate
objects that may comply with the principle under some restriction and whose
entropy is maximal but may be generated recursively from those that are
actually algorithmically random offering a refinement to classical Maxent. We
take advantage of a causal algorithmic calculus to derive a thermodynamic-like
result based on how difficult it is to reprogram a computer code. Using the
distinction between computable and algorithmic randomness we quantify the cost
in information loss associated with reprogramming. To illustrate this we apply
the algorithmic refinement to Maxent on graphs and introduce a Maximal
Algorithmic Randomness Preferential Attachment (MARPA) Algorithm, a
generalisation over previous approaches. We discuss practical implications of
evaluation of network randomness. Our analysis provides insight in that the
reprogrammability asymmetry appears to originate from a non-monotonic
relationship to algorithmic probability. Our analysis motivates further
analysis of the origin and consequences of the aforementioned asymmetries,
reprogrammability, and computation.Comment: 30 page
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