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<p>In this paper our focus is on analysis and design of linear and polyhedral signal recoveries robust with respect to the deterministic uncertainty in the observation matrix. This can be seen as a “deterministic counterpart” of the work [<CitationRef CitationID="CR1">1</CitationRef>] where the case of random uncertainty was studied. We investigate the performance of estimates robust w.r.t. deterministic norm-bounded matrix uncertainty, derive efficiently computable bounds for the estimation risk and discuss the construction of “presumably good” estimates.</p>

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Robust Signal Recovery Under Uncertain-but-Bounded Perturbations in Observation Matrix

  • 期刊论文

  • 发表日期：

    2025年4月22日

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  • Yannis Bekri,
  • Anatoli Juditsky,
  • Arkadi Nemirovski

摘要

In this paper our focus is on analysis and design of linear and polyhedral signal recoveries robust with respect to the deterministic uncertainty in the observation matrix. This can be seen as a “deterministic counterpart” of the work [1] where the case of random uncertainty was studied. We investigate the performance of estimates robust w.r.t. deterministic norm-bounded matrix uncertainty, derive efficiently computable bounds for the estimation risk and discuss the construction of “presumably good” estimates.