13,786 research outputs found
Proof mining in metric fixed point theory and ergodic theory
In this survey we present some recent applications of proof mining to the
fixed point theory of (asymptotically) nonexpansive mappings and to the
metastability (in the sense of Terence Tao) of ergodic averages in uniformly
convex Banach spaces.Comment: appeared as OWP 2009-05, Oberwolfach Preprints; 71 page
Two Forms of Inconsistency in Quantum Foundations
Recently, there has been some discussion of how Dutch Book arguments might be
used to demonstrate the rational incoherence of certain hidden variable models
of quantum theory (Feintzeig and Fletcher 2017). In this paper, we argue that
the 'form of inconsistency' underlying this alleged irrationality is deeply and
comprehensively related to the more familiar 'inconsistency' phenomenon of
contextuality. Our main result is that the hierarchy of contextuality due to
Abramsky and Brandenburger (2011) corresponds to a hierarchy of
additivity/convexity-violations which yields formal Dutch Books of different
strengths. We then use this result to provide a partial assessment of whether
these formal Dutch Books can be interpreted normatively.Comment: 26 pages, 5 figure
Oscillation and the mean ergodic theorem for uniformly convex Banach spaces
Let B be a p-uniformly convex Banach space, with p >= 2. Let T be a linear
operator on B, and let A_n x denote the ergodic average (1 / n) sum_{i< n} T^n
x. We prove the following variational inequality in the case where T is power
bounded from above and below: for any increasing sequence (t_k)_{k in N} of
natural numbers we have sum_k || A_{t_{k+1}} x - A_{t_k} x ||^p <= C || x ||^p,
where the constant C depends only on p and the modulus of uniform convexity.
For T a nonexpansive operator, we obtain a weaker bound on the number of
epsilon-fluctuations in the sequence. We clarify the relationship between
bounds on the number of epsilon-fluctuations in a sequence and bounds on the
rate of metastability, and provide lower bounds on the rate of metastability
that show that our main result is sharp
A theoretical framework for supervised learning from regions
Supervised learning is investigated, when the data are represented not only by labeled points but also labeled regions of the input space. In the limit case, such
regions degenerate to single points and the proposed approach changes back to the classical learning context. The adopted framework entails the minimization
of a functional obtained by introducing a loss function that involves such regions. An additive regularization term is expressed via differential operators that model
the smoothness properties of the desired input/output relationship. Representer
theorems are given, proving that the optimization problem associated to learning
from labeled regions has a unique solution, which takes on the form of a linear
combination of kernel functions determined by the differential operators together
with the regions themselves. As a relevant situation, the case of regions given
by multi-dimensional intervals (i.e., “boxes”) is investigated, which models prior
knowledge expressed by logical propositions
The Goodman-Nguyen Relation within Imprecise Probability Theory
The Goodman-Nguyen relation is a partial order generalising the implication
(inclusion) relation to conditional events. As such, with precise probabilities
it both induces an agreeing probability ordering and is a key tool in a certain
common extension problem. Most previous work involving this relation is
concerned with either conditional event algebras or precise probabilities. We
investigate here its role within imprecise probability theory, first in the
framework of conditional events and then proposing a generalisation of the
Goodman-Nguyen relation to conditional gambles. It turns out that this relation
induces an agreeing ordering on coherent or C-convex conditional imprecise
previsions. In a standard inferential problem with conditional events, it lets
us determine the natural extension, as well as an upper extension. With
conditional gambles, it is useful in deriving a number of inferential
inequalities.Comment: Published version:
http://www.sciencedirect.com/science/article/pii/S0888613X1400101
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