  We show how to perform sparse approximate Gaussian elimination for Laplacian
matrices. We present a simple, nearly linear time algorithm that approximates a
Laplacian by a matrix with a sparse Cholesky factorization, the version of
Gaussian elimination for symmetric matrices. This is the first nearly linear
time solver for Laplacian systems that is based purely on random sampling, and
does not use any graph theoretic constructions such as low-stretch trees,
sparsifiers, or expanders. The crux of our analysis is a novel concentration
bound for matrix martingales where the differences are sums of conditionally
independent variables.
