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A sparsity-exploiting variant of the Bartels—Golub decomposition for linear programming bases
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Sparse Matrices and their Applications
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Some Basic Techniques for Solving Sparse Systems of Linear Equations
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A degeneracy exploiting LU factorization for the simplex method
inferredcites →https://doi.org/10.1007/bf01584086openalex-snapshot
Reinversion with the preassigned pivot procedure
inferredcites →https://doi.org/10.1007/bf01584343openalex-snapshot
On the Bartels—Golub decomposition for linear programming bases
inferredcites →https://doi.org/10.1007/bf01584548openalex-snapshot
Updated triangular factors of the basis to maintain sparsity in the product form simplex method
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A bump triangular dynamic factorization algorithm for the simplex method
inferredcites →https://doi.org/10.1007/bf02169151openalex-snapshot
A stabilization of the simplex method
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Advanced Linear-Programming Computing Techniques
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Large Sparse Sets of Linear Equations
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On the Automatic Scaling of Matrices for Gaussian Elimination
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Pivoting for Size and Sparsity in Linear Programming Inversion Routes
inferredcites →https://doi.org/10.1093/imamat/14.3.281openalex-snapshot
A Comparison of Sparsity Orderings for Obtaining a Pivotal Sequence in Gaussian Elimination
inferredcites →https://doi.org/10.1093/imamat/8.3.374openalex-snapshot
A Note on the Stability of Gaussian Elimination
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Parameterization of the Environment for Transportable Numerical Software
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The Design of the XMP Linear Programming Library
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The Elimination form of the Inverse and its Application to Linear Programming
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A FAST, STABLE IMPLEMENTATION OF THE SIMPLEX METHOD USING BARTELS-GOLUB UPDATING††Research for this paper was supported by the Energy Research and Development Administration Contract AT(04-3)-326 PA #18, at Stanford University.
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Model Averaging in Large Scale Learning
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