Cutting down on Fill Using Nested Dissection: Provably Good Elimination Orderings

  • Ajit Agrawal
  • Philip Klein
  • R. Ravi
Part of the The IMA Volumes in Mathematics and its Applications book series (IMA, volume 56)


In the last two decades, many heuristics have been developed for finding good elimination orderings for sparse Cholesky factorization. These heuristics aim to find elimination orderings with either low fill, low operation count, or low elimination height. Though many heuristics seem to perform well in practice, there has been a marked absence of much theoretical analysis to back these heuristics. Indeed, few heuristics are known to provide any guarantee on the quality of the elimination ordering produced for arbitrary matrices.

In this work, we present the first polynomial-time ordering algorithm that guarantees approximately optimal fill. Our algorithm is a variant of the well-known nested dissection algorithm. Our ordering performs particularly well when the number of elements in each row (and hence each column) of the coefficient matrix is small. Fortunately, many problems in practice, especially those arising from finite-element methods, have such a property due to the physical constraints of the problems being modeled.

Our ordering heuristic guarantees not only low fill, but also approximately optimal operation count, and approximately optimal elimination height. Elimination orderings with small height and low fill are of much interest when performing factorization on parallel machines. No previous ordering heuristic guaranteed even small elimination height.

We will describe our ordering algorithm and prove its performance bounds. We shall also present some experimental results comparing the quality of the orderings produced by our heuristic to those produced by two other well-known heuristics.


SIAM Journal Gaussian Elimination Input Graph Chordal Graph Performance Guarantee 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag New York, Inc. 1993

Authors and Affiliations

  • Ajit Agrawal
    • 1
  • Philip Klein
  • R. Ravi
    • 2
  1. 1.Digital Equipment Corp.Massively Parallel Systems GroupMaynardUSA
  2. 2.Brown UniversityProvidenceUSA

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