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Abstract: Finding interesting features in scale-free graph data has become an … Using a random group of profiles on the career networking website LinkedIn, colleagues at Waterloo University created matrices representing scientists and their listed skills. The “commute time” between two nodes i and j in a network is a sophisticated distance metric which measures the expected time for a random walker to start at i, arrive at j and return back to i. Location: GRVW Conference Room. Led by Van Henson and Geoff Sanders (LLNL). Timothy La Fond, Geoff Sanders, Christine Klymko, Van Emden Henson (LLNL): An Ensemble Framework for Detecting Community Changes in Dynamic Networks; V.M. Bighorn C/1Session 3C: Inverse Problems and Regularization.

Bighorn BSession 9B: Quantum Linear Algebra Algorithms II. We often run into rectangular matrices and nonsymmetric square matrices, whose structure severely complicates the linear algebra.”Says Sanders, “Factoring such matrices introduces imaginary numbers and other ‘ugly’ math that is so difficult that researchers often artificially symmetrize this kind of data to make it easier to deal with, even though doing so skews the results.”In order to find meaning within these data sets, the team has applied the algorithms they developed for symmetric square matrices, but with several key alterations. Dynamic Non-Uniform Randomization in Asynchronous Linear Solvers Smoothing by asynchronous iterations and sparse approximate inverses for fluid dynamics on graphics processing units Redundancy as a Remedy for Delayed Communication in Asynchronous Solvers Block preconditioners for the the coupled Darcy-Stokes problem Block Preconditioning for Implicit Runge-Kutta Methods for Time-Dependent Unconstrained Energy Functional Methods for Massively Parallel Iterative Eigensolvers A System of Semilinear Singularly Perturbed Convection-Diffusion type Generation of compressed stationary fields with circulant embedding Condensed Nonconforming Reformulations for Preconditioning Finite Element Discretization Problems New mixed methods for incompressible hyperelasticiy in terms of Kirchhoff stress An Adaptive Newton Div-Curl Least-Squares Finite Element Method for the Cahn-Hilliard Equation Adaptive Space-Time CFOSLS for Evolution Equations Registration/Welcome Reception (light buffet) [Copper Conference Center]Session 2A: Preconditioning for Multiphysics Problems I. Bighorn BSession 2C: PDE-constrained Optimization. Bighorn BSession 8B: Quantum Linear Algebra Algorithms I. Bighorn C/1Session 8C: Transport and Nuclear Applications.

LLNL Bighorn BSession 6B: Computational Electromagnetics. Finding patterns within massive amounts of unexplored data requires the use of sophisticated linear algebra and presents a unique challenge. Clustering, partitioning, and community identification within this data set was accomplished using the newly developed factorization algorithms in conjunction with algebraic multigrid analysis.Nonsymmetric square matrix analysis presents equally challenging but different hurdles.

UBC. For this problem, the team discovered a factorization of nonsymmetric square matrices that readily reveals the cyclic structure. Van Emden Henson, Geoff Sanders, and their team at Livermore’s Center for Applied Scientific Computing (CASC) have developed improved matrix factorization algorithms to address the common problems encountered when parsing extremely large, … Plenary Session Bighorn BSession 14A: Algorithms/Software for Forward and Inverse Problems. I worked with Geoff Sanders on the implementation of Katz ranking and Centerpiece subgraph algorithms in C++ in LLNL's Glider graph package that is built off of Trilinos. led by Van Henson (LLNL) Topics include: solvers and preconditioning for graph problems, matrix functions for graph analysis, spectral partitioning, eigenvector properties, impact of skewed degree distributions, ranking algorithms, Markov chains ... Alyson Fox, Geoff Sanders and Andrew Knyazev. Bighorn C/1Session 15A: Iterative Methods and Preconditioning. January 1, 2011. Bighorn C/1Session 10B: Quantum Linear Algebra Algorithms III. “What we’ve moved on to with this current project,” adds Henson, “are data sets and matrices that do not behave as nicely. In this example, researchers might want to know what information these documents contain and the most important topics within that information.To uncover this, the team developed a method of building the rectangular matrices into larger matrices and then factoring them so they can be analyzed in a similar way to the symmetric square matrices. Antonio Rosa (Massachusetts Institute of Technology), Geoff Sanders (Lawrence Livermore National Laboratory), Charles Yee (Massachusetts Institute of Technology), and Albert Reuther (Massachusetts Institute of Technology) On Large-Scale Graph Generation with Validation of Diverse Triangle Statistics at Edges and Vertices 287 Bighorn C/2Session 11C: Multigrid Algorithms and their Performance on GPU Platforms. • Geoff Sanders Lawrence Livermore National Laboratory, USA . Bighorn C/1Session 7A: Machine Learning and Iterative Methods. Recommended Citation.

Their current work has significant potential applications in the The team’s previous LDRD-funded work for CASC dealt with data sets referred to as symmetric square matrices, which are “very well-behaved and involve only real numbers in their calculations and therefore are easy to work with on the computer—interpretation is simpler,” explains Sanders.
Bighorn C/1Session 4C: Nonlinear Solvers and Coupling Methods in Climate Modeling.

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