SelInv---An Algorithm for Selected Inversion of a Sparse Symmetric Matrix

Abstract

We describe an efficient implementation of an algorithm for computing selected elements of a general sparse symmetric matrix A that can be decomposed as A = LDLT , where L is lower triangular and D is diagonal. Our implementation, which is called SelInv , is built on top of an efficient supernodal left-looking LDLT factorization of A . We discuss how computational efficiency can be gained by making use of a relative index array to handle indirect addressing. We report the performance of SelInv on a collection of sparse matrices of various sizes and nonzero structures. We also demonstrate how SelInv can be used in electronic structure calculations.