Abstract
Interior methods for linear programming were designed mainly for problems formulated with equality constraints and non-negative variables. The formulation with inequality constraints has shown to be very convenient for practical implementations, and the translation of methods designed for one formulation into the other is not trivial. This paper relates the geometric features of both representations, shows how to transport data and procedures between them and shows how cones and conical projections can be associated with inequality constraints.
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Gonzaga, C.C. Interior point algorithms for linear programming with inequality constraints. Mathematical Programming 52, 209–225 (1991). https://doi.org/10.1007/BF01582888
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DOI: https://doi.org/10.1007/BF01582888