4,438 research outputs found
A multivariate generalization of Costa's entropy power inequality
A simple multivariate version of Costa's entropy power inequality is proved.
In particular, it is shown that if independent white Gaussian noise is added to
an arbitrary multivariate signal, the entropy power of the resulting random
variable is a multidimensional concave function of the individual variances of
the components of the signal. As a side result, we also give an expression for
the Hessian matrix of the entropy and entropy power functions with respect to
the variances of the signal components, which is an interesting result in its
own right.Comment: Proceedings of the 2008 IEEE International Symposium on Information
Theory, Toronto, ON, Canada, July 6 - 11, 200
Free energy and entropy of a dipolar liquid by computer simulations
Thermodynamic properties for a system composed of dipolar molecules are computed. Free energy is evaluated by means of the thermodynamic integration technique, and it is also estimated by using a perturbation theory approach, in which every molecule is modeled as a hard sphere within a square well, with an electric dipole at its center. The hard sphere diameter, the range and depth of the well, and the dipole moment have been calculated from properties easily obtained in molecular dynamics simulations. Connection between entropy and dynamical properties is explored in the liquid and supercooled states by using instantaneous normal mode calculations. A model is proposed in order to analyze translation and rotation contributions to entropy separately. Both contributions decrease upon cooling, and a logarithmic correlation between excess entropy associated with translation and the corresponding proportion of imaginary frequency modes is encountered. Rosenfeld scaling law between reduced diffusion and excess entropy is tested, and the origin of its failure at low temperatures is investigated.Postprint (author's final draft
Distributed Nonconvex Multiagent Optimization Over Time-Varying Networks
We study nonconvex distributed optimization in multiagent networks where the
communications between nodes is modeled as a time-varying sequence of arbitrary
digraphs. We introduce a novel broadcast-based distributed algorithmic
framework for the (constrained) minimization of the sum of a smooth (possibly
nonconvex and nonseparable) function, i.e., the agents' sum-utility, plus a
convex (possibly nonsmooth and nonseparable) regularizer. The latter is usually
employed to enforce some structure in the solution, typically sparsity. The
proposed method hinges on Successive Convex Approximation (SCA) techniques
coupled with i) a tracking mechanism instrumental to locally estimate the
gradients of agents' cost functions; and ii) a novel broadcast protocol to
disseminate information and distribute the computation among the agents.
Asymptotic convergence to stationary solutions is established. A key feature of
the proposed algorithm is that it neither requires the double-stochasticity of
the consensus matrices (but only column stochasticity) nor the knowledge of the
graph sequence to implement. To the best of our knowledge, the proposed
framework is the first broadcast-based distributed algorithm for convex and
nonconvex constrained optimization over arbitrary, time-varying digraphs.
Numerical results show that our algorithm outperforms current schemes on both
convex and nonconvex problems.Comment: Copyright 2001 SS&C. Published in the Proceedings of the 50th annual
Asilomar conference on signals, systems, and computers, Nov. 6-9, 2016, CA,
US
The MIMO Iterative Waterfilling Algorithm
This paper considers the non-cooperative maximization of mutual information
in the vector Gaussian interference channel in a fully distributed fashion via
game theory. This problem has been widely studied in a number of works during
the past decade for frequency-selective channels, and recently for the more
general MIMO case, for which the state-of-the art results are valid only for
nonsingular square channel matrices. Surprisingly, these results do not hold
true when the channel matrices are rectangular and/or rank deficient matrices.
The goal of this paper is to provide a complete characterization of the MIMO
game for arbitrary channel matrices, in terms of conditions guaranteeing both
the uniqueness of the Nash equilibrium and the convergence of asynchronous
distributed iterative waterfilling algorithms. Our analysis hinges on new
technical intermediate results, such as a new expression for the MIMO
waterfilling projection valid (also) for singular matrices, a mean-value
theorem for complex matrix-valued functions, and a general contraction theorem
for the multiuser MIMO watefilling mapping valid for arbitrary channel
matrices. The quite surprising result is that uniqueness/convergence conditions
in the case of tall (possibly singular) channel matrices are more restrictive
than those required in the case of (full rank) fat channel matrices. We also
propose a modified game and algorithm with milder conditions for the uniqueness
of the equilibrium and convergence, and virtually the same performance (in
terms of Nash equilibria) of the original game.Comment: IEEE Transactions on Signal Processing (accepted
Hessian and concavity of mutual information, differential entropy, and entropy power in linear vector Gaussian channels
Within the framework of linear vector Gaussian channels with arbitrary
signaling, closed-form expressions for the Jacobian of the minimum mean square
error and Fisher information matrices with respect to arbitrary parameters of
the system are calculated in this paper. Capitalizing on prior research where
the minimum mean square error and Fisher information matrices were linked to
information-theoretic quantities through differentiation, closed-form
expressions for the Hessian of the mutual information and the differential
entropy are derived. These expressions are then used to assess the concavity
properties of mutual information and differential entropy under different
channel conditions and also to derive a multivariate version of the entropy
power inequality due to Costa.Comment: 33 pages, 2 figures. A shorter version of this paper is to appear in
IEEE Transactions on Information Theor
Optimal Linear Precoding Strategies for Wideband Non-Cooperative Systems based on Game Theory-Part I: Nash Equilibria
In this two-parts paper we propose a decentralized strategy, based on a
game-theoretic formulation, to find out the optimal precoding/multiplexing
matrices for a multipoint-to-multipoint communication system composed of a set
of wideband links sharing the same physical resources, i.e., time and
bandwidth. We assume, as optimality criterion, the achievement of a Nash
equilibrium and consider two alternative optimization problems: 1) the
competitive maximization of mutual information on each link, given constraints
on the transmit power and on the spectral mask imposed by the radio spectrum
regulatory bodies; and 2) the competitive maximization of the transmission
rate, using finite order constellations, under the same constraints as above,
plus a constraint on the average error probability. In Part I of the paper, we
start by showing that the solution set of both noncooperative games is always
nonempty and contains only pure strategies. Then, we prove that the optimal
precoding/multiplexing scheme for both games leads to a channel diagonalizing
structure, so that both matrix-valued problems can be recast in a simpler
unified vector power control game, with no performance penalty. Thus, we study
this simpler game and derive sufficient conditions ensuring the uniqueness of
the Nash equilibrium. Interestingly, although derived under stronger
constraints, incorporating for example spectral mask constraints, our
uniqueness conditions have broader validity than previously known conditions.
Finally, we assess the goodness of the proposed decentralized strategy by
comparing its performance with the performance of a Pareto-optimal centralized
scheme. To reach the Nash equilibria of the game, in Part II, we propose
alternative distributed algorithms, along with their convergence conditions.Comment: Paper submitted to IEEE Transactions on Signal Processing, September
22, 2005. Revised March 14, 2007. Accepted June 5, 2007. To be published on
IEEE Transactions on Signal Processing, 2007. To appear on IEEE Transactions
on Signal Processing, 200
Performance analysis and optimal selection of large mean-variance portfolios under estimation risk
We study the consistency of sample mean-variance portfolios of arbitrarily
high dimension that are based on Bayesian or shrinkage estimation of the input
parameters as well as weighted sampling. In an asymptotic setting where the
number of assets remains comparable in magnitude to the sample size, we provide
a characterization of the estimation risk by providing deterministic
equivalents of the portfolio out-of-sample performance in terms of the
underlying investment scenario. The previous estimates represent a means of
quantifying the amount of risk underestimation and return overestimation of
improved portfolio constructions beyond standard ones. Well-known for the
latter, if not corrected, these deviations lead to inaccurate and overly
optimistic Sharpe-based investment decisions. Our results are based on recent
contributions in the field of random matrix theory. Along with the asymptotic
analysis, the analytical framework allows us to find bias corrections improving
on the achieved out-of-sample performance of typical portfolio constructions.
Some numerical simulations validate our theoretical findings
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