Searcharxiv⌕ Search

arXiv subjects

C. Sinan Gunturk

Publications and source records attributed to C. Sinan Gunturk.

3 recordsLinked to original sources

Iteratively re-weighted least squares minimization for sparse recovery

We analyze an Iteratively Re-weighted Least Squares (IRLS) algorithm for promoting l1-minimization in sparse and compressible vector recovery. We prove its convergence and we estimate its local rate. We show how the algorithm can be modified in order to promote lt-minimization for t<1, and how this modification produces superlinear rates of convergence.

math.NA↗

Approximation by power series with $\pm 1$ coefficients

In this paper we construct certain type of near-optimal approximations of a class of analytic functions in the unit disc by power series with two distinct coefficients. More precisely, we show that if all the coefficients of the power series f(z) are real and lie in [-a,a] where a < 1, then there exists a power series Q(z) with coefficients in {-1,+1} such that |f(z)-Q(z)| approaches 0 at the rate exp(-C/|1-z|) as z approaches 1 non-tangentially inside the unit disc. A result by Borwein-Erdelyi-Kos shows that this type of decay rate is best possible. The special case f=0 yields a near-optimal solution to the "fair duel" problem of Konyagin.

math.CA↗

Ergodic Dynamics in Sigma-Delta Quantization: Tiling Invariant Sets and Spectral Analysis of Error

This paper has two themes that are intertwined: The first is the dynamics of certain piecewise affine maps on the Euclidean space that arise from a class of analog-to-digital conversion methods called Sigma-Delta quantization. The second is the analysis of reconstruction error associated to each such method. Sigma-Delta quantization generates approximate representations of functions by sequences that lie in a restricted set of discrete values. These are special sequences in that their local averages track the function values closely, thus enabling simple convolutional reconstruction. In this paper, we are concerned with the approximation of constant functions only, a basic case that presents surprisingly complex behavior. An m'th order Sigma-Delta scheme with input x can be translated into a dynamical system that produces a discrete-valued sequence (in particular, a 0-1 sequence) q as its output. When the schemes are stable, we show that the underlying piecewise affine maps possess invariant sets that tile the Euclidean space up to a finite multiplicity. When this multiplicity is one (the single-tile case), the dynamics within the tile is isomorphic to that of a generalized skew translation on the m dimensional torus. The value of x can be approximated using any consecutive M elements in q with increasing accuracy in M. We show that the asymptotical behavior of reconstruction error depends on the regularity of the invariant sets, the order m, and some arithmetic properties of x. We determine the behavior in a number of cases of practical interest and provide good upper bounds in some other cases when exact analysis is not yet available.

math.DS↗