Sparse power factorization: balancing peakiness and sample complexity

Jakob Geppert, Felix Krahmer, Dominik Stöger

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


In many applications, one is faced with an inverse problem, where the known signal depends in a bilinear way on two unknown input vectors. Often at least one of the input vectors is assumed to be sparse, i.e., to have only few non-zero entries. Sparse power factorization (SPF), proposed by Lee, Wu, and Bresler, aims to tackle this problem. They have established recovery guarantees for a somewhat restrictive class of signals under the assumption that the measurements are random. We generalize these recovery guarantees to a significantly enlarged and more realistic signal class at the expense of a moderately increased number of measurements.

Original languageEnglish
JournalAdvances in Computational Mathematics
StateAccepted/In press - 2019


  • Bilinear inverse problems
  • Compressed sensing
  • Sparse power factorization


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