Data-sparse matrix computations Lecture 25: Low Rank + Sparse Matrix Recovery

نویسندگان

  • Anil Damle
  • Heather Wilber
  • Lijun Ding
  • Andrew Horning
چکیده

In the previous lecture, we observed that it is possible to recover a sparse solution to Ax = b by solving a minimization problem involving the 1-norm. In this lecture, we consider a matrix A that can be written as A = L+ S, where L is a low rank matrix and S is a sparse matrix, and seek a method that recovers L and S. We remark that Lecture 26 forms a sequal to these notes and addresses the technical details related to recovery under the assumption that only a subset of the entries of A are observable. To motivate this work, we begin with two application-based examples.

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تاریخ انتشار 2017