29,811 research outputs found
Revisiting Random Points: Combinatorial Complexity and Algorithms
Consider a set of points picked uniformly and independently from
for a constant dimension -- such a point set is extremely well
behaved in many aspects. For example, for a fixed , we prove a new
concentration result on the number of pairs of points of at a distance at
most -- we show that this number lies in an interval that contains only
numbers.
We also present simple linear time algorithms to construct the Delaunay
triangulation, Euclidean MST, and the convex hull of the points of . The MST
algorithm is an interesting divide-and-conquer algorithm which might be of
independent interest. We also provide a new proof that the expected complexity
of the Delaunay triangulation of is linear -- the new proof is simpler and
more direct, and might be of independent interest. Finally, we present a simple
time algorithm for the distance selection problem for
Low-rank updates and a divide-and-conquer method for linear matrix equations
Linear matrix equations, such as the Sylvester and Lyapunov equations, play
an important role in various applications, including the stability analysis and
dimensionality reduction of linear dynamical control systems and the solution
of partial differential equations. In this work, we present and analyze a new
algorithm, based on tensorized Krylov subspaces, for quickly updating the
solution of such a matrix equation when its coefficients undergo low-rank
changes. We demonstrate how our algorithm can be utilized to accelerate the
Newton method for solving continuous-time algebraic Riccati equations. Our
algorithm also forms the basis of a new divide-and-conquer approach for linear
matrix equations with coefficients that feature hierarchical low-rank
structure, such as HODLR, HSS, and banded matrices. Numerical experiments
demonstrate the advantages of divide-and-conquer over existing approaches, in
terms of computational time and memory consumption
Online Updating of Statistical Inference in the Big Data Setting
We present statistical methods for big data arising from online analytical
processing, where large amounts of data arrive in streams and require fast
analysis without storage/access to the historical data. In particular, we
develop iterative estimating algorithms and statistical inferences for linear
models and estimating equations that update as new data arrive. These
algorithms are computationally efficient, minimally storage-intensive, and
allow for possible rank deficiencies in the subset design matrices due to
rare-event covariates. Within the linear model setting, the proposed
online-updating framework leads to predictive residual tests that can be used
to assess the goodness-of-fit of the hypothesized model. We also propose a new
online-updating estimator under the estimating equation setting. Theoretical
properties of the goodness-of-fit tests and proposed estimators are examined in
detail. In simulation studies and real data applications, our estimator
compares favorably with competing approaches under the estimating equation
setting.Comment: Submitted to Technometric
The efficiencies of generating cluster states with weak non-linearities
We propose a scalable approach to building cluster states of matter qubits
using coherent states of light. Recent work on the subject relies on the use of
single photonic qubits in the measurement process. These schemes can be made
robust to detector loss, spontaneous emission and cavity mismatching but as a
consequence the overhead costs grow rapidly, in particular when considering
single photon loss. In contrast, our approach uses continuous variables and
highly efficient homodyne measurements. We present a two-qubit scheme, with a
simple bucket measurement system yielding an entangling operation with success
probability 1/2. Then we extend this to a three-qubit interaction, increasing
this probability to 3/4. We discuss the important issues of the overhead cost
and the time scaling. This leads to a "no-measurement" approach to building
cluster states, making use of geometric phases in phase space.Comment: 21 pages, to appear in special issue of New J. Phys. on
"Measurement-Based Quantum Information Processing
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