منابع مشابه
Spectral Compressed Sensing via Projected Gradient Descent
Let x ∈ C be a spectrally sparse signal consisting of r complex sinusoids with or without damping. We consider the spectral compressed sensing problem, which is about reconstructing x from its partial revealed entries. By utilizing the low rank structure of the Hankel matrix corresponding to x, we develop a computationally efficient algorithm for this problem. The algorithm starts from an initi...
متن کاملMaterial for “ Spectral Compressed Sensing via Projected Gradient Descent ”
We extend PGD and its recovery guarantee [1] from one-dimensional spectrally sparse signal recovery to the multi-dimensional case. Assume the underlying multi-dimensional spectrally sparse signal is of model order r and total dimension N . We show that O(r log(N)) measurements are sufficient for PGD to achieve successful recovery with high probability provided the underlying signal satisfies so...
متن کاملFast Binary Compressive Sensing via \ell_0 Gradient Descent
We present a fast Compressive Sensing algorithm for the reconstruction of binary signals {0, 1}-valued binary signals from its linear measurements. The proposed algorithm minimizes a non-convex penalty function that is given by a weighted sum of smoothed l0 norms, under the [0, 1] box-constraint. It is experimentally shown that the proposed algorithm is not only significantly faster than linear...
متن کاملRobust 1-bit Compressive Sensing via Gradient Support Pursuit
This paper studies a formulation of 1-bit Compressive Sensing (CS) problem based on the maximum likelihood estimation framework. In order to solve the problem we apply the recently proposed Gradient Support Pursuit algorithm, with a minor modification. Assuming the proposed objective function has a Stable Restricted Hessian, the algorithm is shown to accurately solve the 1-bit CS problem. Furth...
متن کاملQSGD: Communication-Efficient SGD via Gradient Quantization and Encoding
Parallel implementations of stochastic gradient descent (SGD) have received significant research attention, thanks to its excellent scalability properties. A fundamental barrier when parallelizing SGD is the high bandwidth cost of communicating gradient updates between nodes; consequently, several lossy compresion heuristics have been proposed, by which nodes only communicate quantized gradient...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Physical Review E
سال: 2021
ISSN: 2470-0045,2470-0053
DOI: 10.1103/physreve.103.022405