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.

In this work, we extend the simplex algorithm of linear programming for finding a local minimum of a concave quadratic function subject to box constraints. In order to test the performance of the ...

In methods based on the simplex algorithm, it is not easy to obtain a primal basic feasible solution to the minimization linear programming with intuitionistic fuzzy variables problem with equality ...

Solve linear optimization problems including minimization and maximization with simplex algorithm. Uses the Big M method to solve problems with larger equal constraints in Python ...

Introducing the Pivot Adaptive Method (PAM) - a faster variant of Gabasov's Adaptive Method (AM) for minimizing computation time. Explore the resolution of problems through successive tables and ...

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 ...

This method has undergone so many improvements and has been competitive for solving practical linear programming (LP) problems so far. Interior point method: This is a method that reaches the optimal ...