Calculation of time-distance curves in helioseismology can be formulated as a blind-deconvolution (or system identification) problem. A classical solution in one-dimensional space is Kolmogorov’s Fourier domain spectral-factorization method. The helical coordinate system maps two-dimensions to one. Likewise a three-dimensional volume is representable as a concatenation of many one-dimensional s...

Weak factorization systems Weak factorization systems are of paramount importance to homotopical algebra. This connection is best illustrated by the following definition, due to Joyal and Tierney [JT07]. Definition 1. A Quillen model structure on a category M, with a class of maps W called weak equivalences satisfying the 2-of-3 property, consists of two classes of maps C and F so that (C ∩W,F)...

This paper claims that causal model theory describes the meaningful information of probability distributions after a factorization. If the minimal factorization of a distribution is incompressible, its Kolmogorov minimal sufficient statistics, the parents lists, can be represented by a directed acyclic graph (DAG). We showed that a faithful Bayesian network is a minimal factorization and that a...

We present an algebraic structured preconditioner for the iterative solution of large sparse linear systems. The preconditioner is based on a multifrontal variant of sparse LU factorization used with nested dissection ordering. Multifrontal factorization amounts to a partial factorization of a sequence of logically dense frontal matrices, and the preconditioner is obtained if structured factori...

Steady state simulations in Computational Fluid Dynamics (CFD), which rely on implicit time integration, are not experiencing great accelerations on GPUs. Moreover, most of the reported acceleration effort concerns solving the linear system of equations while neglecting the acceleration potential of running the entire simulation on the GPU. In this paper, we present the software implementation ...

the polynomial interpolation in one dimensional space r is an important method to approximate the functions. the lagrange and newton methods are two well known types of interpolations. in this work, we describe the semi inherited interpolation for approximating the values of a function. in this case, the interpolation matrix has the semi inherited lu factorization.

The block-oriented sparse Cholesky factorization decomposes a sparse matrix into rectangular sub-blocks, and handles each block as a computational unit in order to increase data reuse in a hierarchical memory system. As well, the factorization method increases the degree of concurrency with the reduction of communication volumes so that it performs more eeciently on a distributed-memory multipr...