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Carnegie Mellon Researchers Break Speed Barrier in Solving Important Class of Linear Systems.

 Carnegie Mellon News (PA) (10/21/10) Byron Spice

Carnegie Mellon University (CMU) computer scientists have developed an algorithm that can solve systems of linear equations related to real-world problems in image processing, logistics and scheduling, and recommendation systems. The algorithm, developed by CMU professor Gary Miller, systems scientist Ioannis Koutis, and Ph.D. student Richard Peng, uses new tools from graph theory, randomized algorithms, and linear algebra to make solving real-world problems much faster. The algorithm applies to a class of problems called symmetric diagonally dominant (SSD) systems. “The fast SDD solvers developed by Koutis, Miller, and Peng represent a real breakthrough in this domain, and I expect them to have a major impact on the work that we do,” says Microsoft researcher Richard Szeliski. The CMU team’s approach to solving SDD systems is to first solve a simplified system that can be done rapidly and serve as a preconditioner to guide future steps to an ultimate solution.



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