Beatrice Riviere

Fall 2022

    Meeting time and place:T Th 1pm-2:15pm KCK 101
    Office Hours:by appointment (send email to: riviere at rice dot edu)
    Course Objectives
    This course covers iterative methods for solving linear systems. These methods include Jacobi, Gauss-Seidel, geometric multigrids, algebraic multigrids. The connection between multigrid methods and some neural networks used in machine learning will be highlighted. The course will also introduce randomized methods for matrix computations (such as randomized matrix matrix multiplications, randomized singular value decomposition).
    Course Outcomes
    Upon completion of the course, students have a good understanding of the convergence of various iterative methods, their pros and cons for the solution of linear solvers. They will have developed their own code for relaxation methods and multigrid methods. They will know how to prove error bounds for some randomized algorithms.
    Homeworks (100%).
    Reading Resources
    Matrix Computations by Gene Golub and Charles Van Loan.
    Iterative Methods for Sparse Linear Systems by Yousef Saad.
    A Tutorial on Multigrid Methods by William Briggs.
    Late policy
    Homeworks are to be emailed by midnight on the due date, otherwise they are considered late. Late homeworks will incur penalties in increments of 10% for each late homework.
    If you have a documented disability that may affect academic performance, you should make sure this documentation is on file with Disability Resource Center (Allen Center, Room 111 / / x5841) to determine the accommodations you need, and meet with me to discuss your accommodation needs.
    Title IX
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