Santa Barbara / Bern / New York / Washington / DC / Toledo / Pittsburgh / /
Company
LG / Cambridge University Press / SIAM Journal / Microsoft / /
Country
United States / Canada / / /
Facility
Carnegie Mellon University / University of Illinois / building C / /
IndustryTerm
time cutpreserving sparsification algorithm / faster sparsification algorithm / actual solution / resistance-based algorithm / iterative algorithm / matrixvector products / sparse systems / iterative numerical algorithms / monotonicity law / machinery / matrix-vector products / linear-time algorithm / recursive preconditioning algorithm / nearly-linear time partitioning algorithm / ultrasparsification algorithm / elliptic finite element systems / sparsification algorithm / well known applications / electrical network / nearly-linear time algorithms / binary search / polynomial time algorithm / interior point algorithms / electric networks / image processing / incremental graph sparsification algorithm / sparse linear systems / nested dissection algorithm / approximate solution / ultra-sparsification algorithm / combinatorial algorithm / local partitioning algorithm / parallel algorithm / computing / approximate solutions / linear systems / nearlylinear time algorithms / positive definite systems / nearly-linear time algorithm / /
Organization
Cambridge University / National Science Foundation / University of Illinois / International Congress / American Mathematical Society / Center for Computational Thinking / Carnegie Mellon University / Natural Sciences and Engineering Research Council of Canada / IEEE Computer Society / /
Person
Gary L. Miller Carnegie / Ai / Dan Spielman / Charalampos Tsourakakis / TAL S PARSIFY / Ioannis Koutis Carnegie / Richard Peng Carnegie / /
Approaching Optimality For Solving SDD Linear Systems Ioannis Koutis Carnegie Mellon University Email: [removed] Gary L. Miller