Computing Estimators of Dantzig Selector type via Column and Constraint Generation

arXiv1908.06515MaRDI QIDQ6323836

Author name not available (Why is that?)

Publication date: 18 August 2019

Abstract: We consider a class of linear-programming based estimators in reconstructing a sparse signal from linear measurements. Specific formulations of the reconstruction problem considered here include Dantzig selector, basis pursuit (for the case in which the measurements contain no errors), and the fused Dantzig selector (for the case in which the underlying signal is piecewise constant). In spite of being estimators central to sparse signal processing and machine learning, solving these linear programming problems for large scale instances remains a challenging task, thereby limiting their usage in practice. We show that classic constraint- and column-generation techniques from large scale linear programming, when used in conjunction with a commercial implementation of the simplex method, and initialized with the solution from a closely-related Lasso formulation, yields solutions with high efficiency in many settings.

Has companion code repository: https://github.com/atzheng/DantzigLP

This page was built for publication: Computing Estimators of Dantzig Selector type via Column and Constraint Generation

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6323836)