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Smallest singular value of sparse random matrices

  • Author / Creator
    Rivasplata, Omar D
  • In this thesis probability estimates on the smallest singular value of random matrices with independent entries are extended to a class of sparse random matrices. We show that one can relax a previously used condition of uniform boundedness of the variances from below. This allows us to consider matrices with null entries or, more generally, with entries having small variances. Our results do not assume identical distribution of the entries of a random matrix, and help to clarify the role of the variances in the corresponding estimates. We also show that it is enough to require boundedness from above of the r-th moment of the entries, for some r > 2.

  • Subjects / Keywords
  • Graduation date
    2012-06
  • Type of Item
    Thesis
  • Degree
    Doctor of Philosophy
  • DOI
    https://doi.org/10.7939/R3FQ04
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Doctoral
  • Department
    • Department of Mathematical and Statistical Sciences
  • Specialization
    • Mathematics
  • Supervisor / co-supervisor and their department(s)
    • Nicole Tomczak-Jaegermann (Mathematical and Statistical Sciences)
    • Alexander Litvak (Mathematical and Statistical Sciences)
  • Examining committee members and their departments
    • Vlad Yaskin (Mathematical and Statistical Sciences)
    • Alexander Penin (Physics)
    • Vladimir Troitsky (Mathematical and Statistical Sciences)
    • Mark Meckes (External Examiner)