A review of sparse recovery algorithms
Nowadays, a large amount of information has to be transmitted or processed. This implies
high-power processing, large memory density, and increased energy consumption. In …
high-power processing, large memory density, and increased energy consumption. In …
An introduction to compressive sampling
Conventional approaches to sampling signals or images follow Shannon's theorem: the
sampling rate must be at least twice the maximum frequency present in the signal (Nyquist …
sampling rate must be at least twice the maximum frequency present in the signal (Nyquist …
Compressive sensing: From theory to applications, a survey
Compressive sensing (CS) is a novel sampling paradigm that samples signals in a much
more efficient way than the established Nyquist sampling theorem. CS has recently gained a …
more efficient way than the established Nyquist sampling theorem. CS has recently gained a …
Stable signal recovery from incomplete and inaccurate measurements
Suppose we wish to recover a vector x0∈ ℝ𝓂 (eg, a digital signal or image) from incomplete
and contaminated observations y= A x0+ e; A is an 𝓃× 𝓂 matrix with far fewer rows than …
and contaminated observations y= A x0+ e; A is an 𝓃× 𝓂 matrix with far fewer rows than …
Matrix completion with noise
On the heels of compressed sensing, a new field has very recently emerged. This field
addresses a broad range of problems of significant practical interest, namely, the recovery of …
addresses a broad range of problems of significant practical interest, namely, the recovery of …
CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
Compressive sampling offers a new paradigm for acquiring signals that are compressible
with respect to an orthonormal basis. The major algorithmic challenge in compressive …
with respect to an orthonormal basis. The major algorithmic challenge in compressive …
The Dantzig selector: Statistical estimation when p is much larger than n
In many important statistical applications, the number of variables or parameters p is much
larger than the number of observations n. Suppose then that we have observations y= Xβ+ z …
larger than the number of observations n. Suppose then that we have observations y= Xβ+ z …
[PDF][PDF] Compressive sampling
Conventional wisdom and common practice in acquisition and reconstruction of images from
frequency data follow the basic principle of the Nyquist density sampling theory. This …
frequency data follow the basic principle of the Nyquist density sampling theory. This …
Data streams: Algorithms and applications
In the data stream scenario, input arrives very rapidly and there is limited memory to store
the input. Algorithms have to work with one or few passes over the data, space less than …
the input. Algorithms have to work with one or few passes over the data, space less than …
Beyond Nyquist: Efficient sampling of sparse bandlimited signals
Wideband analog signals push contemporary analog-to-digital conversion (ADC) systems to
their performance limits. In many applications, however, sampling at the Nyquist rate is …
their performance limits. In many applications, however, sampling at the Nyquist rate is …