This paper by Sepandar Kamvar, Taher Haveliwala, and Gene Golub describes an algorithm to speed up the computation of PageRank using the fact that pages converge at different rates.

This paper by Sepandar Kamvar and Taher Haveliwala proves analytically the second eigenvalue of the Google Matrix, which has implications for the PageRank algorithm.

By CCRMA: The Stanford University Center for Computer Research in Music and Acoustics.

Stanford Exploration Project library, containing basic seismic data processing routines such as phase shift migration and velocity stacking. Includes documentation, source code, and binary packages for Mac OS X and Cygwin.

By Rick Ottolini, based on an article by Gilbert Strang.

Updates a dense square factorization L*C = U, when rows and columns of C are added, deleted or replaced.

Code for sparse linear equations (symmetric or general) and sparse least squares, and updating a dense square factorization L C = U.

Solves a set of sparse, symmetric linear equations using a conjugate gradient type method.

Solves sparse linear equations using a conjugate-gradient type method.

Solves unsymmetric equations, linear least squares, and damped least squares for a left-hand-side matrix that is large and sparse.