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Compressed sensing with sparse corruptions: Fault-tolerant sparse collocation approximations (Technical Report) | OSTI.GOV

机译:具有稀疏损坏的压缩感知:容错稀疏配置近似(技术报告)| OsTI.GOV

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摘要

The recovery of approximately sparse or compressible coefficients in a polynomial chaos expansion is a common goal in many modern parametric uncertainty quantification (UQ) problems. However, relatively little effort in UQ has been directed toward theoretical and computational strategies for addressing the sparse corruptions problem, where a small number of measurements are highly corrupted. Such a situation has become pertinent today since modern computational frameworks are sufficiently complex with many interdependent components that may introduce hardware and software failures, some of which can be difficult to detect and result in a highly polluted simulation result. In this paper we present a novel compressive sampling-based theoretical analysis for a regularized t1 minimization algorithm that aims to recover sparse expansion coefficients in the presence of measurement corruptions. Our recovery results are uniform (the theoretical guarantees hold for all compressible signals and compressible corruptions vectors), and prescribe algorithmic regularization parameters in terms of a user-defined

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  • 作者单位
  • 年(卷),期 2000(),
  • 年度 2000
  • 页码
  • 总页数 27
  • 原文格式 PDF
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  • 入库时间 2022-08-19 17:01:47
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