575 research outputs found
On the performance of algorithms for the minimization of -penalized functionals
The problem of assessing the performance of algorithms used for the
minimization of an -penalized least-squares functional, for a range of
penalty parameters, is investigated. A criterion that uses the idea of
`approximation isochrones' is introduced. Five different iterative minimization
algorithms are tested and compared, as well as two warm-start strategies. Both
well-conditioned and ill-conditioned problems are used in the comparison, and
the contrast between these two categories is highlighted.Comment: 18 pages, 10 figures; v3: expanded version with an additional
synthetic test problem
Compressed sensing imaging techniques for radio interferometry
Radio interferometry probes astrophysical signals through incomplete and
noisy Fourier measurements. The theory of compressed sensing demonstrates that
such measurements may actually suffice for accurate reconstruction of sparse or
compressible signals. We propose new generic imaging techniques based on convex
optimization for global minimization problems defined in this context. The
versatility of the framework notably allows introduction of specific prior
information on the signals, which offers the possibility of significant
improvements of reconstruction relative to the standard local matching pursuit
algorithm CLEAN used in radio astronomy. We illustrate the potential of the
approach by studying reconstruction performances on simulations of two
different kinds of signals observed with very generic interferometric
configurations. The first kind is an intensity field of compact astrophysical
objects. The second kind is the imprint of cosmic strings in the temperature
field of the cosmic microwave background radiation, of particular interest for
cosmology.Comment: 10 pages, 1 figure. Version 2 matches version accepted for
publication in MNRAS. Changes includes: writing corrections, clarifications
of arguments, figure update, and a new subsection 4.1 commenting on the exact
compliance of radio interferometric measurements with compressed sensin
Optimal incorporation of sparsity information by weighted optimization
Compressed sensing of sparse sources can be improved by incorporating prior
knowledge of the source. In this paper we demonstrate a method for optimal
selection of weights in weighted norm minimization for a noiseless
reconstruction model, and show the improvements in compression that can be
achieved.Comment: 5 pages, 2 figures, to appear in Proceedings of ISIT201
Compressed Sensing Based on Random Symmetric Bernoulli Matrix
The task of compressed sensing is to recover a sparse vector from a small
number of linear and non-adaptive measurements, and the problem of finding a
suitable measurement matrix is very important in this field. While most recent
works focused on random matrices with entries drawn independently from certain
probability distributions, in this paper we show that a partial random
symmetric Bernoulli matrix whose entries are not independent, can be used to
recover signal from observations successfully with high probability. The
experimental results also show that the proposed matrix is a suitable
measurement matrix.Comment: arXiv admin note: text overlap with arXiv:0902.4394 by other author
- …