Non-negative Sparse Recovery at Minimal Sampling Rate
<|reference_start|>Non-negative Sparse Recovery at Minimal Sampling Rate: It is known that sparse recovery is possible if the number of measurements is in the order of the sparsity, but the corresponding decoders either lack polynomial decoding time or robustness to noise. Commonly, decoders that rely on a null space property are being used. These achieve polynomial time decoding and are robust to additive noise but pay the price by requiring more measurements. The non-negative least residual has been established as such a decoder for non-negative recovery. A new equivalent condition for uniform, robust recovery of non-negative sparse vectors with the non-negative least residual that is not based on null space properties is introduced. It is shown that the number of measurements for this equivalent condition only needs to be in the order of the sparsity. Further, it is explained why the robustness to additive noise is similar, but not equal, to the robustness of decoders based on null space properties.<|reference_end|>
@article{zarucha2024non-negative,
    title={Non-negative Sparse Recovery at Minimal Sampling Rate},
    author={Hendrik Bernd Zarucha and Peter Jung},
    journal={arXiv preprint arXiv:2409.00503},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00503},
    primaryClass={cs.IT math.IT}
}