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Proof-of-concept implementation of the (dual) simplex algorithm for linear programming in Julia.

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jlSimplex

Proof-of-concept implementation of the (dual) simplex algorithm for linear programming in Julia. It's not guaranteed to solve your problem, but it can solve some NETLIB instances.

This is not a textbook implementation and contains some advanced features that are important for practical implementations, such as:

  • Stabilizing 2-pass ratio test
  • Dual steepest-edge pricing
  • Cost shifting for anti-degeneracy and numerical stability
  • LU factorized basis matrix with product-form updates

For the expert audience, here's what's needed before we can compete with state-of-the-art implementations:

  • Bound-flipping ratio test
  • Internal rescaling and anti-degeneracy perturbations
  • Primal simplex implementation (for clean up)
  • Markowitz-type LU factorization
  • Suhl-Suhl (Forrest-Tomlin)-style updates
  • Exploitation of hyper-sparsity
  • Presolve

There is no reason to use this code to solve real problems. It's here as a proof of concept of a large, complex application in Julia. Both algorithmic and performance-tuning contributions are welcome.

Current timings for test.jl solving GREENBEA.SIF on a laptop (Intel i5-3320M):

  • jlSimplex: 9.83 seconds
  • GLPK: 1.39 seconds

jlSimplex is released under the terms of the MIT license.

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Proof-of-concept implementation of the (dual) simplex algorithm for linear programming in Julia.

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