bundles / scipy 1.17.1 / scipy / optimize / _linprog_ip
module
scipy.optimize._linprog_ip
Members
Summary
Interior-point method for linear programming
Extended Summary
The interior-point method uses the primal-dual path following algorithm outlined in [1]. This algorithm supports sparse constraint matrices and is typically faster than the simplex methods, especially for large, sparse problems. Note, however, that the solution returned may be slightly less accurate than those of the simplex methods and will not, in general, correspond with a vertex of the polytope defined by the constraints.
Additional content
Interior-point method for linear programming
The interior-point method uses the primal-dual path following algorithm outlined in [1]. This algorithm supports sparse constraint matrices and is typically faster than the simplex methods, especially for large, sparse problems. Note, however, that the solution returned may be slightly less accurate than those of the simplex methods and will not, in general, correspond with a vertex of the polytope defined by the constraints.
References
Andersen, Erling D., and Knud D. Andersen. "The MOSEK interior point optimizer for linear programming: an implementation of the homogeneous algorithm." High performance optimization. Springer US, 2000. 197-232.
Aliases
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scipy.optimize._linprog_ip