Ravibabu Mashetti Assistant Professor Department of Mathematics

Dr. Ravibabu Mashetti is an Assistant professor of Mathematics at Mahindra University. He has completed his Post-Doctoral Research from the Indian Institute of Science, Bangalore, in the Computational Statistical Physics. His Ph.D. thesis is to devise and analyze new algorithms for computing eigenvalues and eigenvectors of large sparse matrices. The methods developed are applied to benchmark matrices from several areas of Mathematics and Engineering. The comparison of numerical results demonstrated the efficiency of the devised algorithms. 

His publications are Mashetti Ravibabu and Arindama Singh, On refined Ritz vectors and polynomial characterization, Computers and Mathematics with Applications, 67(2014), no. 5, 1057-1064. Mashetti Ravibabu and Arindama Singh, A new variant of Arnoldi method for approximation of eigenpairs, Journal of Computational and Applied Mathematics, 344(2018), 424-437. Mashetti Ravibabu and Arindama Singh, The Least-squares and line search in extracting eigenpairs in Jacobi-Davidson method, BIT J. Numerical Mathematics.

  • Education
    1. Indian Institute of Science, Bangalore, India July 2017- Oct 2019
      • Post-Doctoral Research Fellow, Computational Statistical Physics Lab, IISC, Bangalore
    2. Indian Institute of Technology, Kanpur, India July 2016-June 2017
      • Post-Doctoral Research Fellow, Dept of Mathematics and Statistics, IIT Kanpur
    3. Indian Institute of Technology, Madras, India
      • Pre/Post-Doctoral Research Fellow, July 2015-June2016 Dept of Mathematics,
      • IIT Madras Senior Research Fellow, Dept of Mathematics, Jan 2013 - July 2015
      • IIT Madras Junior Research Fellow, Dept of Mathematics, Jan 2011 - Jan 2013
    4. Vaagdevi Degree and P.G. College, Kakatiya University, Warangal Master of Science (Mathematics) June 2004 - June 2006
    5. Bachelor of Science June 2001 - June 2004 (Mathematics, Electronics, Computer Science)
  • Research
    • Numerical Linear Algebra 
    • Machine Learning
  • Publications
    • Mashetti Ravibabu and Arindama Singh, A new variant of Arnoldi method for approximation of eigenpairs, Journal of Computational and Applied Mathematics, 344(2018), 424-437. 
    • Mashetti Ravibabu and Arindama Singh, On refined Ritz vectors and polynomial characterization, Computers and Mathematics with Applications, 67(2014), no. 5, 1057-1064. 
    • Mashetti Ravibabu and Arindama Singh, The Least-squares and line search in extracting eigenpairs in Jacobi-Davidson method, BIT J. Numerical Mathematics, Accepted 
    • Mashetti Ravibabu, A modification of the Jacobi-Davidson method, (https://arxiv.org/abs/1902.02285)
    • Mashetti Ravibabu, On residual norms in the Rayleigh-Ritz and refined projection methods for linear eigenvalue problems, (https://arxiv.org/abs/1902.08057) 
    • Mashetti Ravibabu, On Harmonic Ritz vectors and the stagnation of GMRES, (https://arxiv.org/abs/1902.08049) 
    • Mashetti Ravibabu, GMRES with Singular vectors, (https://arxiv.org/abs/1902.02260) 
    • Mashetti Ravibabu, Stability analysis of improved Two-level orthogonal Arnoldi procedure, (https://arxiv.org/abs/1902.01766)

    Invited Talks

    • Tata Institute of Fundamental Research- International Centre for Theoretical Sciences, Bengaluru, 6 January 2016.
  • Experience

    Working Experience

    1. SRI CHAITANYA IIT-JEE COACHING CENTER, June 2006 - June 2010 Lecturer in Mathematics, Vijayawada Branches.

    Research Experience

    1. PhD Thesis topic:
      New Algorithms to Compute Eigenpairs of Large Matrices 
      This thesis aims to devise and analyze new algorithms for computing eigenvalues and eigenvectors of large sparse matrices. The methods designed are applied to benchmark matrices from several areas of Mathematics and Engineering. The comparison of numerical results demonstrated the efficiency of the developed algorithms.
    2. Post-Doctoral Research work: This work solved a long-standing open problem in computing eigenvalues of large sparse matrices. It also significantly improved one of the well-known sparse linear system solvers and produced an efficient algorithm for solving quadratic eigenvalue problems.