Methods for simplex type dual problems of linear optimization channelled and sparse

Two-side constraint and sparse linear optimization problems, the main object of this work, appear in several applications, such as, production planning problems, mix problems among others. Dual Simplex-typed methods, called two-side constraint dual simplex methods with piecewise linear search were proposed and analyzed in Sousa et al., which showed effective results for dense and small problems and now they are analyzed for larger and sparse problems. These methods were implemented together with some pivoting heuristics to maintain sparsity and to reduce the running time. Sets of linear optimization randomly generated problems with sparse structures that occur in real world were used to analyze the performance of the methods. The computational results show the efficiency of the approaches.