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摘要
In this paper we introduce a novel adaptive approach for solving l(1)-minimization problems as frequently arising in compressed sensing, which is based on the recently introduced inverse scale space method. The scheme allows to efficiently compute minimizers by solving a sequence of low-dimensional nonnegative least-squares problems. %26lt;br%26gt;We provide a detailed convergence analysis in a general setup as well as refined results under special conditions. In addition, we discuss experimental observations in several numerical examples.
- 出版日期2013-1
