Linear programming is a technique for finding the optimal value of an objective function subject to a set of constraints. The dual simplex method is a variation of the simplex method that can be ...

This README introduces the Simplex Method, a popular algorithm for solving linear programming problems in R. Linear programming optimizes an objective function, such as maximizing or minimizing a ...

The dual simplex method, unlike the standard simplex method, starts with an infeasible but optimal (or better) solution for the objective function in a linear programming problem.

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.

The linear semidefinite programming problem is considered. The primal and dual simplex-like algorithms are proposed for its solution. Both algorithms are generalizations of well-known simplex methods ...

It is known that the simplex method requires an exponential number of iterations for some special linear programming instances. Hence the method is neither polynomial nor a strongly-polynomial ...

We present an approach for and minimizing the complementary pivots in a simplex based solution method for a quadratic programming problem. The linearization of the problem is slightly changed from the ...

Discover an efficient active-set approach for nonnegative and general linear programming. Our method adds constraints iteratively, resulting in faster computation compared to previous algorithms.

Overview This README introduces the Simplex Method, a popular algorithm for solving linear programming problems in R. Linear programming optimizes an objective function, such as maximizing or ...

This study proposes a novel technique for solving linear programming problems in a fully fuzzy environment. A modified version of the well-known dual simplex method is used for solving fuzzy linear ...