1st Edition

Random Circulant Matrices

By Arup Bose, Koushik Saha Copyright 2019
    212 Pages
    by Chapman & Hall

    212 Pages
    by Chapman & Hall

    Circulant matrices have been around for a long time and have been extensively used in many scientific areas. This book studies the properties of the eigenvalues for various types of circulant matrices, such as the usual circulant, the reverse circulant, and the k-circulant when the dimension of the matrices grow and the entries are random.



    In particular, the behavior of the spectral distribution, of the spectral radius and of the appropriate point processes are developed systematically using the method of moments and the various powerful normal approximation results. This behavior varies according as the entries are independent, are from a linear process, and are light- or heavy-tailed.



    Arup Bose obtained his B.Stat., M.Stat. and Ph.D. degrees from the Indian Statistical Institute. He has been on its faculty at the Theoretical Statistics and Mathematics Unit, Kolkata, India since 1991. He is a Fellow of the Institute of Mathematical Statistics, and of all three national science academies of India. He is a recipient of the S.S. Bhatnagar Prize and the C.R. Rao Award. He is the author of three books: Patterned Random Matrices, Large Covariance and Autocovariance Matrices (with Monika Bhattacharjee) and U-Statistics, M_m-Estimators and Resampling (with Snigdhansu Chatterjee).



    Koushik Saha obtained a B.Sc. in Mathematics from Ramakrishna Mission Vidyamandiara, Belur and an M.Sc. in Mathematics from Indian Institute of Technology Bombay. He obtained his Ph.D. degree from the Indian Statistical Institute under the supervision of Arup Bose. His thesis on circulant matrices received high praise from the reviewers. He has been on the faculty of the Department of Mathematics, Indian Institute of Technology Bombay since 2014.





    1. Circulants


    2. Circulant



      Symmetric circulant



      Reverse circulant



      k-circulant



      Exercises







    3. Symmetric and reverse circulant




    4. Spectral distribution



      Moment method



      Scaling



      Input and link



      Trace formula and circuits



      Words and vertices



      (M) and Riesz’s condition



      (M) condition



      Reverse circulant



      Symmetric circulant



      Related matrices



      Reduced moment



      A metric



      Minimal condition



      Exercises







    5. LSD: normal approximation




    6. Method of normal approximation



      Circulant



      k-circulant



      Exercises







    7. LSD: dependent input




    8. Spectral density



      Circulant



      Reverse circulant



      Symmetric circulant



      k-circulant



      Exercises







    9. Spectral radius: light tail




    10. Circulant and reverse circulant



      Symmetric circulant



      Exercises







    11. Spectral radius: k-circulant




    12. Tail of product



      Additional properties of the k-circulant



      Truncation and normal approximation



      Spectral radius of the k-circulant



      k-circulant for sn = kg +



      Exercises







    13. Maximum of scaled eigenvalues: dependent input




    14. Dependent input with light tail



      Reverse circulant and circulant



      Symmetric circulant



      k-circulant



      k-circulant for n = k +



      k-circulant for n = kg + , g >



      Exercises







    15. Poisson convergence




    16. Point Process



      Reverse circulant



      Symmetric circulant



      k-circulant, n = k +



      Reverse circulant: dependent input



      Symmetric circulant: dependent input



      k-circulant, n = k + : dependent input



      Exercises







    17. Heavy tailed input: LSD




    18. Stable distribution and input sequence



      Background material



      Reverse circulant and symmetric circulant



      k-circulant: n = kg +



      Proof of Theorem



      Contents vii



      k-circulant: n = kg



      Tail of the LSD



      Exercises







    19. Heavy-tailed input: spectral radius




    20. Input sequence and scaling



      Reverse circulant and circulant



      Symmetric circulant



      Heavy-tailed: dependent input



      Exercises







    21. Appendix




              Proof of Theorem



              Standard notions and results



    Biography

    Arup Bose obtained his B.Stat., M.Stat. and Ph.D. degrees from the Indian Statistical Institute. He has been on its faculty at the Theoretical Statistics and Mathematics Unit, Kolkata, India since 1991. He is a Fellow of the Institute of Mathematical Statistics, and of all three national science academies of India. He is a recipient of the S.S. Bhatnagar Prize and the C.R. Rao Award. He is the author of three books: Patterned Random Matrices, Large Covariance and Autocovariance Matrices (with Monika Bhattacharjee) and U-Statistics, M_m-Estimators and Resampling (with Snigdhansu Chatterjee).

    Koushik Saha obtained a B.Sc. in Mathematics from Ramakrishna Mission Vidyamandiara, Belur and an M.Sc. in Mathematics from Indian Institute of Technology Bombay. He obtained his Ph.D. degree from the Indian Statistical Institute under the supervision of Arup Bose. His thesis on circulant matrices received high praise from the reviewers. He has been on the faculty of the Department of Mathematics, Indian Institute of Technology Bombay since 2014.