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Publication Information
Title Probing For Trace Estimation of a Permuted Matrix Inverse Corresponding to a Lattice Displacement
Abstract Probing is a general technique that is used to reduce the variance of the Hutchinson stochastic estimator for the trace of the inverse of a large, sparse matrix A. The variance of the estimator is the sum of the squares of the off-diagonal elements of inv(A) . Therefore, this technique computes probing vectors that when used in the estimator they annihilate the largest off-diagonal elements. For matrices that display decay of the magnitude of |A^{-1}_{ ij}| with the graph distance between nodes i and j, this is achieved through graph coloring of increasing powers A^p. Equivalently, when a matrix stems from a lattice discretization, it is computationally beneficial to find a distance-p coloring of the lattice. In [23] a hierarchical coloring was proposed so that p can be increased at runtime as needed without discarding previous work. In this work, we study probing for the more general problem of computing the trace of a permutation of inv(A), say P*inv(A) . The motivation comes fro
Author(s) Heather Switzer, Andreas Stathopoulos, Eloy Romero, Jesse Laeuchli, Konstantinos Orginos
Publication Date August 2022
Document Type Journal Article
Primary Institution Thomas Jefferson National Accelerator Facility, Newport News
Affiliation Comp Sci&Tech (CST) Div / Scientific Computing / Scientific Computing
Funding Source Other, SURA, Exascale Computing Project
Proprietary? No
This publication conveys Technical Science Results
Document Numbers
JLAB Number: JLAB-CST-21-3500 OSTI Number: 1897613
LANL Number: arXiv:2106.01275 Other Number: DOE/OR/23177-5320
Associated with an experiment No
Associated with EIC No
Supported by Jefferson Lab LDRD Funding No
Journal Article
Journal Name SIAM Journal on Scientfic Computing
Refereed No
Volume 44
Issue 4
Page(s) B1096-B1121
Attachments/Datasets/DOI Link
Document(s)
DisplacementPaper-4.pdf (STI Document)
2106.01275.pdf (Accepted Manuscript)
DOI Link
Dataset(s) (none)
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