Numerical Analysis: Linear Algebra

CS 383C / CAM 383C / M 383E

Unique Numbers: 55820(CS) / 67280(CAM) / 59295(Math)

Course Announcement

Fall 2008
MWF 9-10am
Room: ENS 126

Instructor: Inderjit Dhillon (send email)
Office: ACES 2.332
Office Hours: MW 10-11am

TA: Prateek Jain (send email)
Office: TAY 137
Office Hours: M 2-3:30pm, W 3:30-5pm


  • Numerical Linear Algebra by L. N. Trefethen and D. Bau, SIAM, 1997.
  • Handouts

  • Class Survey
  • Homeworks


  • Fundamentals (Vectors, matrices, norms, singular value decomposition).
  • QR Factorization and Least Squares (Gram-Schmidt orthgonalization, Householder tridiagonalization, least squares).
  • Conditioning and Stability (Condition numbers, floating point arithmetic, analysis of specific algorithms).
  • Solving systems of equations (Gaussian Elimination, pivoting, stability, Cholesky factorization).
  • The Eigenvalue Problem (Reduction to Hessenberg or Tridiagonal form, bisection+inverse iteration, Rayleigh quotient iteration, QR algorithm, SVD computation).
  • Iterative Methods.
  • Grading

  • 40% midterms (2 exams)
  • 35% final exam
  • 20% homeworks
  • 5% class participation and attendance
  • Other Books

  • Fundamentals of Matrix Computations by David Watkins, 2nd Ed., 2002. Very readable textbook.
  • Applied Numerical Linear Algebra by James W. Demmel, SIAM, 1997.
  • Matrix Computations by G. Golub and C. Van Loan, 3rd Ed. Johns Hopkins Press, 1996. Encylopedic reference for matrix computations.
  • Related Material

  • Strang's Video Lectures on Linear Algebra, Undergraduate course, MIT, Fall 1999.
  • Code of Conduct: