Preprint

A linear programming approach to sparse linear regression with quantized data

V. CeronePolitecnico di Torino, Dipartimento di Automatica e Informatica, corso Duca degli Abruzzi 24, Torino, 10129, ItalySophie M. FossonPolitecnico di Torino, Dipartimento di Automatica e Informatica, corso Duca degli Abruzzi 24, Torino, 10129, ItalyD. RegrutoPolitecnico di Torino, Dipartimento di Automatica e Informatica, corso Duca degli Abruzzi 24, Torino, 10129, Italy

2019en

ABI

Abstract

The sparse linear regression problem is difficult to handle with usual sparse optimization models when both predictors and measurements are either quantized or represented in low-precision, due to non-convexity. In this paper, we provide a novel linear programming approach, which is effective to tackle this problem. In particular, we prove theoretical guarantees of robustness, and we present numerical results that show improved performance with respect to the state-of-the-art methods.

Topics

Sparse and Compressive Sensing Techniques Distributed Sensor Networks and Detection Algorithms Statistical Methods and Inference

Identifiers

DOI: 10.23919/acc.2019.8815117

Citations and references

Cited by 034 references

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