# Computing and analyzing recoverable supports for sparse reconstruction

@article{Kruschel2015ComputingAA, title={Computing and analyzing recoverable supports for sparse reconstruction}, author={Christian Kruschel and Dirk A. Lorenz}, journal={Advances in Computational Mathematics}, year={2015}, volume={41}, pages={1119-1144} }

Designing computational experiments involving ℓ1 minimization with linear constraints in a finite-dimensional, real-valued space for receiving a sparse solution with a precise number k of nonzero entries is, in general, difficult. Several conditions were introduced which guarantee that, for example for small k or for certain matrices, simply placing entries with desired characteristics on a randomly chosen support will produce vectors which can be recovered by ℓ1 minimization. In this work, we…

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A new subgradient method for the minimization of nonsmooth convex functions over a convex set using adaptive approximate projections only requiring to move within a certain distance of the exact projections (which decreases in the course of the algorithm) is proposed.

Erratum to: Advances in Computational Mathematics volume 41 December 2015, issue 6

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- 2016

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