Sparse vector method improvements via minimum inverse fill-in ordering

A practical ordering algorithm to enhance sparse vector methods without sacrificing the sparsity of the table of factors is presented. The proposed algorithm locally minimizes the number of new nonzero elements in the inverse of the lower triangular matrix during the factorization process. Two refined versions which can usually give the shortest length on factorization path of single and/or composite singletons are provided. Test results from previously published ordering algorithms based on minimum fill-in are also presented for comparison. The performance of applications to power system state estimation is evaluated. It is shown that the proposed ordering algorithm is a very effective strategy for the improvement of sparse vector methods