110,495 research outputs found
Quantifying Homology Classes
We develop a method for measuring homology classes. This involves three
problems. First, we define the size of a homology class, using ideas from
relative homology. Second, we define an optimal basis of a homology group to be
the basis whose elements' size have the minimal sum. We provide a greedy
algorithm to compute the optimal basis and measure classes in it. The algorithm
runs in time, where is the size of the simplicial
complex and is the Betti number of the homology group. Third, we
discuss different ways of localizing homology classes and prove some hardness
results
P?=NP as minimization of degree 4 polynomial, integration or Grassmann number problem, and new graph isomorphism problem approaches
While the P vs NP problem is mainly approached form the point of view of
discrete mathematics, this paper proposes reformulations into the field of
abstract algebra, geometry, fourier analysis and of continuous global
optimization - which advanced tools might bring new perspectives and approaches
for this question. The first one is equivalence of satisfaction of 3-SAT
problem with the question of reaching zero of a nonnegative degree 4
multivariate polynomial (sum of squares), what could be tested from the
perspective of algebra by using discriminant. It could be also approached as a
continuous global optimization problem inside , for example in
physical realizations like adiabatic quantum computers. However, the number of
local minima usually grows exponentially. Reducing to degree 2 polynomial plus
constraints of being in , we get geometric formulations as the
question if plane or sphere intersects with . There will be also
presented some non-standard perspectives for the Subset-Sum, like through
convergence of a series, or zeroing of fourier-type integral for some natural . The last discussed
approach is using anti-commuting Grassmann numbers , making nonzero only if has a Hamilton cycle. Hence,
the PNP assumption implies exponential growth of matrix representation of
Grassmann numbers. There will be also discussed a looking promising
algebraic/geometric approach to the graph isomorphism problem -- tested to
successfully distinguish strongly regular graphs with up to 29 vertices.Comment: 19 pages, 8 figure
Factory of realities: on the emergence of virtual spatiotemporal structures
The ubiquitous nature of modern Information Retrieval and Virtual World give
rise to new realities. To what extent are these "realities" real? Which
"physics" should be applied to quantitatively describe them? In this essay I
dwell on few examples. The first is Adaptive neural networks, which are not
networks and not neural, but still provide service similar to classical ANNs in
extended fashion. The second is the emergence of objects looking like
Einsteinian spacetime, which describe the behavior of an Internet surfer like
geodesic motion. The third is the demonstration of nonclassical and even
stronger-than-quantum probabilities in Information Retrieval, their use.
Immense operable datasets provide new operationalistic environments, which
become to greater and greater extent "realities". In this essay, I consider the
overall Information Retrieval process as an objective physical process,
representing it according to Melucci metaphor in terms of physical-like
experiments. Various semantic environments are treated as analogs of various
realities. The readers' attention is drawn to topos approach to physical
theories, which provides a natural conceptual and technical framework to cope
with the new emerging realities.Comment: 21 p
Geometric Approach to Digital Quantum Information
We present geometric methods for uniformly discretizing the continuous
N-qubit Hilbert space. When considered as the vertices of a geometrical figure,
the resulting states form the equivalent of a Platonic solid. The
discretization technique inherently describes a class of pi/2 rotations that
connect neighboring states in the set, i.e. that leave the geometrical figures
invariant. These rotations are shown to generate the Clifford group, a general
group of discrete transformations on N qubits. Discretizing the N-qubit Hilbert
space allows us to define its digital quantum information content, and we show
that this information content grows as N^2. While we believe the discrete sets
are interesting because they allow extra-classical behavior--such as quantum
entanglement and quantum parallelism--to be explored while circumventing the
continuity of Hilbert space, we also show how they may be a useful tool for
problems in traditional quantum computation. We describe in detail the discrete
sets for one and two qubits.Comment: Introduction rewritten; 'Sample Application' section added. To appear
in J. of Quantum Information Processin
Coherent sets for nonautonomous dynamical systems
We describe a mathematical formalism and numerical algorithms for identifying
and tracking slowly mixing objects in nonautonomous dynamical systems. In the
autonomous setting, such objects are variously known as almost-invariant sets,
metastable sets, persistent patterns, or strange eigenmodes, and have proved to
be important in a variety of applications. In this current work, we explain how
to extend existing autonomous approaches to the nonautonomous setting. We call
the new time-dependent slowly mixing objects coherent sets as they represent
regions of phase space that disperse very slowly and remain coherent. The new
methods are illustrated via detailed examples in both discrete and continuous
time
Algebraic statistical models
Many statistical models are algebraic in that they are defined in terms of
polynomial constraints, or in terms of polynomial or rational parametrizations.
The parameter spaces of such models are typically semi-algebraic subsets of the
parameter space of a reference model with nice properties, such as for example
a regular exponential family. This observation leads to the definition of an
`algebraic exponential family'. This new definition provides a unified
framework for the study of statistical models with algebraic structure. In this
paper we review the ingredients to this definition and illustrate in examples
how computational algebraic geometry can be used to solve problems arising in
statistical inference in algebraic models
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