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Found 6 papers, 2 papers with code
Binary input reconstruction for linear systems: a performance analysis
Recovering the digital input of a time-discrete linear system from its (noisy) output is a significant challenge in the fields of data transmission, deconvolution, channel equalization, and inverse modeling.
Optimization and Control Systems and Control Systems and Control
Centralized and distributed online learning for sparse time-varying optimization
The development of online algorithms to track time-varying systems has drawn a lot of attention in the last years, in particular in the framework of online convex optimization.
Sparse linear regression with compressed and low-precision data via concave quadratic programming
We consider the problem of the recovery of a k-sparse vector from compressed linear measurements when data are corrupted by a quantization noise.
Recovery of binary sparse signals from compressed linear measurements via polynomial optimization
The recovery of signals with finite-valued components from few linear measurements is a problem with widespread applications and interesting mathematical characteristics.
A biconvex analysis for Lasso l1 reweighting
In this letter, we propose a new convergence analysis of a Lasso l1 reweighting method, based on the observation that the algorithm is an alternated convex search for a biconvex problem.
GPU-Accelerated Algorithms for Compressed Signals Recovery with Application to Astronomical Imagery Deblurring
Finally, we practically demonstrate our algorithms in a typical application of circulant matrices: deblurring a sparse astronomical image in the compressed domain.
