On the minimum FLOPs problem in the sparse Cholesky factorization
Provides theoretical clarity for sparse matrix computation practitioners and algorithm designers by rigorously differentiating two key optimization metrics.
The paper proves that minimizing FLOPs and minimizing fill in sparse Cholesky factorization are distinct problems, with no ordering optimal for both, and establishes NP-hardness of minimizing FLOPs.
Prior to computing the Cholesky factorization of a sparse, symmetric positive definite matrix, a reordering of the rows and columns is computed so as to reduce both the number of fill elements in Cholesky factor and the number of arithmetic operations (FLOPs) in the numerical factorization. These two metrics are clearly somehow related and yet it is suspected that these two problems are different. However, no rigorous theoretical treatment of the relation of these two problems seems to have been given yet. In this paper we show by means of an explicit, scalable construction that the two problems are different in a very strict sense. In our construction no ordering, that is optimal for the fill, is optimal with respect to the number of FLOPs, and vice versa. Further, it is commonly believed that minimizing the number of FLOPs is no easier than minimizing the fill (in the complexity sense), but so far no proof appears to be known. We give a reduction chain that shows the NP hardness of minimizing the number of arithmetic operations in the Cholesky factorization.