Use of linear programming models has grown so extensively in recent years that the whole concept for organizing a computer code has undergone a radical change. It no longer is adequate merely to ...

Standard computer implementations of Dantzig's simplex method for linear programming are based upon forming the inverse of the basic matrix and updating the inverse after every step of the method.

This paper shows a method for solving linear programming problems that includes Interval Type-2 fuzzy constraints. The proposed method finds an optimal solution in these conditions using convex ...

A modified version of the well-known dual simplex method is used for solving fuzzy linear programming problems. The use of a ranking function together with the Gaussian elimination process helps in ...

However, standard FOMs, such as the primal-dual hybrid gradient (PDHG) method, are not yet reliable for LP problems, solving only a small fraction of instances. Google researchers introduce PDLP ...

It is proved that this method gives the same sequence of iterations as formulation of the simplex method for quadratic programming given earlier by Dantzing and the authors, which may be called the ...

Discover how fuzzy programming methods, such as Chandra Sen's and statistical averaging, can convert multi-objective linear programming problems into single objective functions. Explore numerical and ...

3 key takeaways Linear programming is an optimization method for achieving the best outcome in a model with linear relationships. The technique involves defining an objective function to maximize ...