On the solution of highly degenerate linear programs
Mathematical Programming: Series A and B
Progress in the dual simplex method for large scale LP problems: practical dual phase 1 algorithms
Computational Optimization and Applications
Dynamic Aggregation of Set-Partitioning Constraints in Column Generation
Operations Research
Bi-dynamic constraint aggregation and subproblem reduction
Computers and Operations Research
A new version of the Improved Primal Simplex for degenerate linear programs
Computers and Operations Research
Multi-phase dynamic constraint aggregation for set partitioning type problems
Mathematical Programming: Series A and B
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Since its appearance in 1947, the primal simplex algorithm has been one of the most popular algorithms for solving linear programs. It is often very efficient when there is very little degeneracy, but it often struggles in the presence of high degeneracy, executing many pivots without improving the objective function value. In this paper, we propose an improved primal simplex algorithm that deals with this issue. This algorithm is based on new theoretical results that shed light on how to reduce the negative impact of degeneracy. In particular, we show that, from a nonoptimal basic solution with p positive-valued variables, there exists a sequence of at most m-p + 1 simplex pivots that guarantee the improvement of the objective value, where m is the number of constraints in the linear program. These pivots can be identified by solving an auxiliary linear program. Finally, we briefly summarize computational results that show the effectiveness of the proposed algorithm on degenerate linear programs.