40,434 research outputs found
Applicability of fair simulation
AbstractIn this paper we compare four notions of fair simulation: direct [9], delay [12], game [19], and exists [16]. Our comparison refers to three main aspects: The time complexity of constructing the fair simulation, the ability to use it for minimization, and the relationship between the fair simulations and universal branching-time logics. We developed a practical application that is based on this comparison. The application is a new implementation for the assume-guarantee modular framework presented By Grumberg at al. in [ACM Transactions on Programming Languages and Systems (TOPLAS), 16 (1994) 843]. The new implementation significantly improves the complexity of the framework
Multicast Multigroup Beamforming for Per-antenna Power Constrained Large-scale Arrays
Large in the number of transmit elements, multi-antenna arrays with
per-element limitations are in the focus of the present work. In this context,
physical layer multigroup multicasting under per-antenna power constrains, is
investigated herein. To address this complex optimization problem
low-complexity alternatives to semi-definite relaxation are proposed. The goal
is to optimize the per-antenna power constrained transmitter in a maximum
fairness sense, which is formulated as a non-convex quadratically constrained
quadratic problem. Therefore, the recently developed tool of feasible point
pursuit and successive convex approximation is extended to account for
practical per-antenna power constraints. Interestingly, the novel iterative
method exhibits not only superior performance in terms of approaching the
relaxed upper bound but also a significant complexity reduction, as the
dimensions of the optimization variables increase. Consequently, multicast
multigroup beamforming for large-scale array transmitters with per-antenna
dedicated amplifiers is rendered computationally efficient and accurate. A
preliminary performance evaluation in large-scale systems for which the
semi-definite relaxation constantly yields non rank-1 solutions is presented.Comment: submitted to IEEE SPAWC 2015. arXiv admin note: substantial text
overlap with arXiv:1406.755
A Direct Estimation Approach to Sparse Linear Discriminant Analysis
This paper considers sparse linear discriminant analysis of high-dimensional
data. In contrast to the existing methods which are based on separate
estimation of the precision matrix \O and the difference \de of the mean
vectors, we introduce a simple and effective classifier by estimating the
product \O\de directly through constrained minimization. The
estimator can be implemented efficiently using linear programming and the
resulting classifier is called the linear programming discriminant (LPD) rule.
The LPD rule is shown to have desirable theoretical and numerical properties.
It exploits the approximate sparsity of \O\de and as a consequence allows
cases where it can still perform well even when \O and/or \de cannot be
estimated consistently. Asymptotic properties of the LPD rule are investigated
and consistency and rate of convergence results are given. The LPD classifier
has superior finite sample performance and significant computational advantages
over the existing methods that require separate estimation of \O and \de.
The LPD rule is also applied to analyze real datasets from lung cancer and
leukemia studies. The classifier performs favorably in comparison to existing
methods.Comment: 39 pages.To appear in Journal of the American Statistical Associatio
Lagrangian reconstruction of cosmic velocity fields
We discuss a Lagrangian reconstruction method of the velocity field from
galaxy redshift catalog that takes its root in the Euler equation. This results
in a ``functional'' of the velocity field which must be minimized. This is
helped by an algorithm solving the minimization of cost-flow problems. The
results obtained by applying this method to cosmological problems are shown and
boundary effects happening in real observational cases are then discussed.
Finally, a statistical model of the errors made by the reconstruction method is
proposed.Comment: 5 pages, 5 figures, contribution to the conference "Euler's
Equations: 250 Years On" (see http://www.obs-nice.fr/etc7/EE250/); to be
published in a special issue of Physica D containing the proceedings of that
conferenc
Buffered Simulation Games for B\"uchi Automata
Simulation relations are an important tool in automata theory because they
provide efficiently computable approximations to language inclusion. In recent
years, extensions of ordinary simulations have been studied, for instance
multi-pebble and multi-letter simulations which yield better approximations and
are still polynomial-time computable.
In this paper we study the limitations of approximating language inclusion in
this way: we introduce a natural extension of multi-letter simulations called
buffered simulations. They are based on a simulation game in which the two
players share a FIFO buffer of unbounded size. We consider two variants of
these buffered games called continuous and look-ahead simulation which differ
in how elements can be removed from the FIFO buffer. We show that look-ahead
simulation, the simpler one, is already PSPACE-hard, i.e. computationally as
hard as language inclusion itself. Continuous simulation is even EXPTIME-hard.
We also provide matching upper bounds for solving these games with infinite
state spaces.Comment: In Proceedings AFL 2014, arXiv:1405.527
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