Steven T. Smith

TL;DR: The techniques and analysis presented in this thesis provide new methods to solve optimization problems posed on Riemannian manifolds, which are applied to the subspace tracking problem found in adaptive signal processing and adaptive control and to several eigenvalue and singular value problems posed as constrained optimization problems.

TL;DR: This paper addresses the problem of computing optimal phase- only adaptive weight vectors by exploiting several properties of phasor and matrix algebra by introducing two new algorithms (the phase-only conjugate gradient and phase-Only Newton's method).

TL;DR: The proposed Subgraph Isomorphism Graph Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a graph challenge that is reflective of many real-world graph analytics processing systems.

TL;DR: The Subgraph Isomorphism Graph Challenge (SIGG) as discussed by the authors is a benchmark for graph analytic systems that can be used to measure and quantitatively compare a wide range of present day and future systems.

TL;DR: This paper describes a graph partition challenge with a baseline partition algorithm of sub-quadratic complexity that employs rigorous Bayesian inferential methods based on a statistical model that captures characteristics of the real-world graphs.