22,611 research outputs found
Revisiting the Complexity of Stability of Continuous and Hybrid Systems
We develop a framework to give upper bounds on the "practical" computational
complexity of stability problems for a wide range of nonlinear continuous and
hybrid systems. To do so, we describe stability properties of dynamical systems
using first-order formulas over the real numbers, and reduce stability problems
to the delta-decision problems of these formulas. The framework allows us to
obtain a precise characterization of the complexity of different notions of
stability for nonlinear continuous and hybrid systems. We prove that bounded
versions of the stability problems are generally decidable, and give upper
bounds on their complexity. The unbounded versions are generally undecidable,
for which we give upper bounds on their degrees of unsolvability
Polynomial Time corresponds to Solutions of Polynomial Ordinary Differential Equations of Polynomial Length
We provide an implicit characterization of polynomial time computation in
terms of ordinary differential equations: we characterize the class
of languages computable in polynomial time in terms of
differential equations with polynomial right-hand side.
This result gives a purely continuous (time and space) elegant and simple
characterization of . This is the first time such classes
are characterized using only ordinary differential equations. Our
characterization extends to functions computable in polynomial time over the
reals in the sense of computable analysis. This extends to deterministic
complexity classes above polynomial time.
This may provide a new perspective on classical complexity, by giving a way
to define complexity classes, like , in a very simple
way, without any reference to a notion of (discrete) machine. This may also
provide ways to state classical questions about computational complexity via
ordinary differential equations, i.e.~by using the framework of analysis
Kolmogorov's Structure Functions and Model Selection
In 1974 Kolmogorov proposed a non-probabilistic approach to statistics and
model selection. Let data be finite binary strings and models be finite sets of
binary strings. Consider model classes consisting of models of given maximal
(Kolmogorov) complexity. The ``structure function'' of the given data expresses
the relation between the complexity level constraint on a model class and the
least log-cardinality of a model in the class containing the data. We show that
the structure function determines all stochastic properties of the data: for
every constrained model class it determines the individual best-fitting model
in the class irrespective of whether the ``true'' model is in the model class
considered or not. In this setting, this happens {\em with certainty}, rather
than with high probability as is in the classical case. We precisely quantify
the goodness-of-fit of an individual model with respect to individual data. We
show that--within the obvious constraints--every graph is realized by the
structure function of some data. We determine the (un)computability properties
of the various functions contemplated and of the ``algorithmic minimal
sufficient statistic.''Comment: 25 pages LaTeX, 5 figures. In part in Proc 47th IEEE FOCS; this final
version (more explanations, cosmetic modifications) to appear in IEEE Trans
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Bounded time computation on metric spaces and Banach spaces
We extend the framework by Kawamura and Cook for investigating computational
complexity for operators occurring in analysis. This model is based on
second-order complexity theory for functions on the Baire space, which is
lifted to metric spaces by means of representations. Time is measured in terms
of the length of the input encodings and the required output precision. We
propose the notions of a complete representation and of a regular
representation. We show that complete representations ensure that any
computable function has a time bound. Regular representations generalize
Kawamura and Cook's more restrictive notion of a second-order representation,
while still guaranteeing fast computability of the length of the encodings.
Applying these notions, we investigate the relationship between purely metric
properties of a metric space and the existence of a representation such that
the metric is computable within bounded time. We show that a bound on the
running time of the metric can be straightforwardly translated into size bounds
of compact subsets of the metric space. Conversely, for compact spaces and for
Banach spaces we construct a family of admissible, complete, regular
representations that allow for fast computation of the metric and provide short
encodings. Here it is necessary to trade the time bound off against the length
of encodings
A Survey on Continuous Time Computations
We provide an overview of theories of continuous time computation. These
theories allow us to understand both the hardness of questions related to
continuous time dynamical systems and the computational power of continuous
time analog models. We survey the existing models, summarizing results, and
point to relevant references in the literature
On zeros of Martin-L\"of random Brownian motion
We investigate the sample path properties of Martin-L\"of random Brownian
motion. We show (1) that many classical results which are known to hold almost
surely hold for every Martin-L\"of random Brownian path, (2) that the effective
dimension of zeroes of a Martin-L\"of random Brownian path must be at least
1/2, and conversely that every real with effective dimension greater than 1/2
must be a zero of some Martin-L\"of random Brownian path, and (3) we will
demonstrate a new proof that the solution to the Dirichlet problem in the plane
is computable
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