We consider linear equations y = Φα where y is a given vector in R n, Φ is a given n by m matrix with n < m ≤ An, and we wish to solve for α ∈ R m. We suppose that the columns of Φ are normalized to unit ℓ 2 norm 1 and we place uniform measure on such Φ. We prove the existence of ρ = ρ(A) so

We present an iterative procedure which asymptotically scales the infinity norm of both rows and columns in a matrix to 1. This scaling strategy exhibits some optimality properties and additionally preserves symmetry. The algorithm also shows fast linear convergence with an asymptotic rate of 1

, where r is the residual vector y − A˜x and t is a positive scalar. We show that if A obeys a uniform uncertainty principle (with unit-normed columns) and if the true parameter vector x is sufficiently sparse (which here roughly guarantees that the model is identifiable), then with very large probability

? To recover x 0 , we consider the solution x to the 1 -regularization problem where is the size of the error term e. We show that if A obeys a uniform uncertainty principle (with unit-normed columns) and if the vector x 0 is sufficiently sparse, then the solution is within the noise level As a first example

to solve in general. In this paper we consider a convex optimization formulation to splitting the specified matrix into its components, by minimizing a linear combination of the ℓ1 norm and the nuclear norm of the components. We develop a notion of rank-sparsity incoherence, expressed as an uncertainty

description of a low-rank approximation D to A, and which are qualitatively faster than the SVD. Both algorithms have provable bounds for the error matrix A D . For any matrix X , let kXk and kXk 2 denote its Frobenius norm and its spectral norm, respectively. In the rst algorithm, c = O(1

and the corresponding c rows of G. An important aspect of the algorithm is the probability distribution used to randomly sample the columns; we will use a judiciously-chosen and data-dependent nonuniform probability distribution. Let F denote the spectral norm and the Frobenius norm, respectively, of a matrix

the familiar maximum-Euclidean-distance norm. Our construction begins with the first signal in the constellation—an oblong complex-valued matrix whose columns are orthonormal—and systematically produces the remaining signals by successively rotating this signal in a high-dimensional complex space

At the previous conference my purpose was to give a rhetorical interpretation to the sacred geometry of the west façade of the Parthenon, the best-known of all Greek tempies, the apogee of Hellenic architecture, built by architects Ichtinus and Callicrates for Perieles, the client, from 447-32 BC, on the Acropolis in Athens.1 In the present paper I wil! take as my point of departure the analysis of the diagram of the west (or east) façade of the Parthen-superior "high" ldeas representation "Iow" inferior Figure 1 demigods/underworld

. On the other hand, the ℓ 1 minimization problem min �α�1 subject to �y − Φα�2 ≤ ɛ, is convex, and is considered tractable. We show that for most Φ the solution ˆα1,ɛ = ˆα1,ɛ(y, Φ) of this problem is quite generally a good approximation for ˆα0,ɛ. We suppose that the columns of Φ are normalized to unit ℓ 2 norm