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A Simple, Combinatorial Algorithm for Solving SDD Systems in Nearly-Linear Time

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arxiv 1301.6628 v1 pith:J7QN5F5U submitted 2013-01-28 cs.DS cs.NAmath.NA

classification cs.DScs.NAmath.NA
keywords algorithmsimpletimecombinatoriallinearnearly-linearsystemsanalysis
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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.

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  1. An Empirical Study of Conjugate Gradient Preconditioners for Solving Symmetric Positive Definite Systems of Linear Equations

    math.NA 2025-05 conditional novelty 5.0 of 10

    Incomplete Cholesky and its modified variant provide the largest work reduction among black-box preconditioners for CG, but fail on roughly 20% of problems, and AMD matrix ordering substantially outperforms RCM.

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