A Simple, Combinatorial Algorithm for Solving SDD Systems in Nearly-Linear Time by Jonathan A. Kelner, Lorenzo Orecchia, Aaron Sidford, Zeyuan Allen Zhu.
Abstract:
In this paper, we present a simple combinatorial algorithm that solves symmetric diagonally dominant (SDD) linear systems in nearly-linear time. It uses very little of the machinery that previously appeared to be necessary for a such an algorithm. It does not require recursive preconditioning, spectral sparsification, or even the Chebyshev Method or Conjugate Gradient. After constructing a “nice” spanning tree of a graph associated with the linear system, the entire algorithm consists of the repeated application of a simple (non-recursive) update rule, which it implements using a lightweight data structure. The algorithm is numerically stable and can be implemented without the increased bit-precision required by previous solvers. As such, the algorithm has the fastest known running time under the standard unit-cost RAM model. We hope that the simplicity of the algorithm and the insights yielded by its analysis will be useful in both theory and practice.
In one popular account, the importance of the discovery was described this way:
The real value of the MIT paper, Spielman says, is in its innovative theoretical approach. “My work and the work of the folks at Carnegie Mellon, we’re solving a problem in numeric linear algebra using techniques from the field of numerical linear algebra,” he says. “Jon’s paper is completely ignoring all of those techniques and really solving this problem using ideas from data structures and algorithm design. It’s substituting one whole set of ideas for another set of ideas, and I think that’s going to be a bit of a game-changer for the field. Because people will see there’s this set of ideas out there that might have application no one had ever imagined.”
Thirty-two pages of tough sledding but if the commentaries are correct, this paper may have a major impact on graph processing.