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Balancing Sparsity and Rank Constraints in Quadratic Basis Pursuit

Abstract : We investigate the methods that simultaneously enforce sparsity and low-rank structure in a matrix as often employed for sparse phase retrieval problems or phase calibration problems in compressive sensing. We propose a new approach for analyzing the trade off between the sparsity and low rank constraints in these approaches which not only helps to provide guidelines to adjust the weights between the aforementioned constraints, but also enables new simulation strategies for evaluating performance. We then provide simulation results for phase retrieval and phase calibration cases both to demonstrate the consistency of the proposed method with other approaches and to evaluate the change of performance with different weights for the sparsity and low rank structure constraints.
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Submitted on : Wednesday, March 19, 2014 - 10:41:26 AM
Last modification on : Thursday, October 27, 2022 - 3:45:34 AM
Long-term archiving on: : Thursday, June 19, 2014 - 11:23:15 AM


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  • HAL Id : hal-00960272, version 2
  • ARXIV : 1403.4267


Cagdas Bilen, Gilles Puy, Rémi Gribonval, Laurent Daudet. Balancing Sparsity and Rank Constraints in Quadratic Basis Pursuit. 2014. ⟨hal-00960272v2⟩



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