Mathematical Programming

, Volume 56, Issue 1–3, pp 1–30 | Cite as

A hierarchical algorithm for making sparse matrices sparser

  • S. Frank Chang
  • S. Thomas McCormick


IfA is the (sparse) coefficient matrix of linear equality constraints, for what nonsingularT isÂTA as sparse as possible, and how can it be efficiently computed? An efficient algorithm for thisSparsity Problem (SP) would be a valuable pre-processor for linearly constrained optimization problems. In this paper we develop a two-pass approach to solve SP. Pass 1 builds a combinatorial structure on the rows ofA which hierarchically decomposes them into blocks. This determines the structure of the optimal transformation matrixT. In Pass 2, we use the information aboutT as a road map to do block-wise partial Gauss-Jordan elimination onA. Two block-aggregation strategies are also suggested that could further reduce the time spend in Pass 2. Computational results indicate that this approach to increasing sparsity produces significant net reductions in simplex solution time.


Linear Equality Mathematical Method Computational Result Equality Constraint Efficient Algorithm 
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

© The Mathematical Programming Society, Inc. 1992

Authors and Affiliations

  • S. Frank Chang
    • 1
  • S. Thomas McCormick
    • 2
  1. 1.GTE Laboratories, Inc.WalthamUSA
  2. 2.Management Science Division, Faculty of Commerce and Business AdministrationThe University of British ColumbiaVancouverCanada

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