Results 1  10
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44
Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
, 2004
"... Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear m ..."
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Cited by 1513 (20 self)
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Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear measurements do we need to recover objects from this class to within accuracy ɛ? This paper shows that if the objects of interest are sparse or compressible in the sense that the reordered entries of a signal f ∈ F decay like a powerlaw (or if the coefficient sequence of f in a fixed basis decays like a powerlaw), then it is possible to reconstruct f to within very high accuracy from a small number of random measurements. typical result is as follows: we rearrange the entries of f (or its coefficients in a fixed basis) in decreasing order of magnitude f  (1) ≥ f  (2) ≥... ≥ f  (N), and define the weakℓp ball as the class F of those elements whose entries obey the power decay law f  (n) ≤ C · n −1/p. We take measurements 〈f, Xk〉, k = 1,..., K, where the Xk are Ndimensional Gaussian
Nonasymptotic theory of random matrices: extreme singular values
 PROCEEDINGS OF THE INTERNATIONAL CONGRESS OF MATHEMATICIANS
, 2010
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Random matrix theory
, 2005
"... Random matrix theory is now a big subject with applications in many disciplines of science, engineering and finance. This article is a survey specifically oriented towards the needs and interests of a numerical analyst. This survey includes some original material not found anywhere else. We includ ..."
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Cited by 82 (4 self)
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Random matrix theory is now a big subject with applications in many disciplines of science, engineering and finance. This article is a survey specifically oriented towards the needs and interests of a numerical analyst. This survey includes some original material not found anywhere else. We include the important mathematics which is a very modern development, as well as the computational software that is transforming the theory into useful practice.
A randomized Kaczmarz algorithm with exponential convergence
"... The Kaczmarz method for solving linear systems of equations is an iterative algorithm that has found many applications ranging from computer tomography to digital signal processing. Despite the popularity of this method, useful theoretical estimates for its rate of convergence are still scarce. We i ..."
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Cited by 68 (1 self)
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The Kaczmarz method for solving linear systems of equations is an iterative algorithm that has found many applications ranging from computer tomography to digital signal processing. Despite the popularity of this method, useful theoretical estimates for its rate of convergence are still scarce. We introduce a randomized version of the Kaczmarz method for consistent, overdetermined linear systems and we prove that it converges with expected exponential rate. Furthermore, this is the first solver whose rate does not depend on the number of equations in the system. The solver does not even need to know the whole system, but only a small random part of it. It thus outperforms all previously known methods on general extremely overdetermined systems. Even for moderately overdetermined systems, numerical simulations as well as theoretical analysis reveal that our algorithm can converge faster than the celebrated conjugate gradient algorithm. Furthermore, our theory and numerical simulations confirm a prediction of Feichtinger et al. in the context of reconstructing bandlimited functions from nonuniform sampling. ∗ T.S. was supported by NSF DMS grant 0511461. R.V. was supported by the Alfred P.
Compressed sensing: how sharp is the restricted isometry property?
, 2009
"... Compressed sensing is a recent technique by which signals can be measured at a rate proportional to their information content, combining the important task of compression directly into the measurement process. Since its introduction in 2004 there have been hundreds of manuscripts on compressed sens ..."
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Cited by 51 (7 self)
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Compressed sensing is a recent technique by which signals can be measured at a rate proportional to their information content, combining the important task of compression directly into the measurement process. Since its introduction in 2004 there have been hundreds of manuscripts on compressed sensing, a large fraction of which have focused on the design and analysis of algorithms to recover a signal from its compressed measurements. The Restricted Isometry Property (RIP) has become a ubiquitous property assumed in their analysis. We present the best known bounds on the RIP, and in the process illustrate the way in which the combinatorial nature of compressed sensing is controlled. Our quantitative bounds on the RIP allow precise statements as to how aggressively a signal can be undersampled, the essential question for practitioners.
On the Capacity Achieving Covariance Matrix for Rician MIMO Channels: An Asymptotic Approach
, 2008
"... In this contribution, the capacityachieving input covariance matrices for coherent blockfading correlated MIMO Rician channels are determined. In contrast with the Rayleigh and uncorrelated Rician cases, no closedform expressions for the eigenvectors of the optimum input covariance matrix are avai ..."
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Cited by 43 (19 self)
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In this contribution, the capacityachieving input covariance matrices for coherent blockfading correlated MIMO Rician channels are determined. In contrast with the Rayleigh and uncorrelated Rician cases, no closedform expressions for the eigenvectors of the optimum input covariance matrix are available. Classically, both the eigenvectors and eigenvalues are computed by numerical techniques. As the corresponding optimization algorithms are not very attractive, an approximation of the average mutual information is evaluated in this paper in the asymptotic regime where the number of transmit and receive antennas converge to + ∞ at the same rate. New results related to the accuracy of the corresponding large system approximation are provided. An attractive optimization algorithm of this approximation is proposed and we establish that it yields an effective way to compute the capacity achieving covariance matrix for the average mutual information. Finally, numerical simulation results show that, even for a moderate number of transmit and receive antennas, the new approach provides the same results as direct maximization approaches of the average mutual information, while being much more computationally attractive.
FROM RANDOM MATRICES TO STOCHASTIC OPERATORS
"... Abstract. We propose that classical random matrix models are properly viewed as finite difference schemes for stochastic differential operators. Three particular stochastic operators commonly arise, each associated with a familiar class of local eigenvalue behavior. The stochastic Airy operator disp ..."
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Cited by 34 (3 self)
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Abstract. We propose that classical random matrix models are properly viewed as finite difference schemes for stochastic differential operators. Three particular stochastic operators commonly arise, each associated with a familiar class of local eigenvalue behavior. The stochastic Airy operator displays soft edge behavior, associated with the Airy kernel. The stochastic Bessel operator displays hard edge behavior, associated with the Bessel kernel. The article concludes with suggestions for a stochastic sine operator, which would display bulk behavior, associated with the sine kernel. 1.
Tails of condition number distributions
 SIAM J. Matrix Anal. Appl
"... Abstract. Let κ be the condition number of an mbyn matrix with independent standard Gaussian entries, either real (β = 1) or complex (β = 2). The major result is the existence of a constant C (depending on m, n, β) such that P [κ> x] < C x −β for all x. As x → ∞, the bound is asymptotically ..."
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Cited by 28 (2 self)
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Abstract. Let κ be the condition number of an mbyn matrix with independent standard Gaussian entries, either real (β = 1) or complex (β = 2). The major result is the existence of a constant C (depending on m, n, β) such that P [κ> x] < C x −β for all x. As x → ∞, the bound is asymptotically tight. An analytic expression is given for the constant C, and simple estimates are given, one involving a TracyWidom largest eigenvalue distribution. All of the results extend beyond real and complex entries to general β.
Condition numbers of random triangular matrices
 SIAM Journal on Matrix Analysis and Applications
, 1998
"... Abstract. Let Ln be a lower triangular matrix of dimension n each of whose nonzero entries is an independent N(0, 1) variable, i.e., a random normal variable of mean 0 and variance 1. It is shown that κn, the 2norm condition number of Ln, satisfies n√ κn → 2 almost surely as n →∞. This exponential ..."
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Cited by 26 (6 self)
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Abstract. Let Ln be a lower triangular matrix of dimension n each of whose nonzero entries is an independent N(0, 1) variable, i.e., a random normal variable of mean 0 and variance 1. It is shown that κn, the 2norm condition number of Ln, satisfies n√ κn → 2 almost surely as n →∞. This exponential growth of κn with n is in striking contrast to the linear growth of the condition numbers of random dense matrices with n that is already known. This phenomenon is not due to small entries on the diagonal (i.e., small eigenvalues) of Ln. Indeed, it is shown that a lower triangular matrix of dimension n whose diagonal entries are fixed at 1 with the subdiagonal entries taken as independent N(0, 1) variables is also exponentially ill conditioned with the 2norm condition number κn of such a matrix satisfying n√ κn → 1.305683410... almost surely as n → ∞. A similar pair of results about complex random triangular matrices is established. The results for real triangular matrices are generalized to triangular matrices with entries from any symmetric, strictly stable distribution.