Kitahara and Mizuno get upper bounds for the maximum number of different basic feasible solutions generated by Dantzig�s simplex method. In this paper, we obtain lower bounds of the maximum number. Part of the results in this paper are shown in a paper by the authors as a quick report without proof. They present a … Read more
We show that the simplex method can be interpreted as a cutting-plane method, assumed that a special pricing rule is used. This approach is motivated by the recent success of the cutting-plane method in the solution of special stochastic programming problems. We compare the classic Dantzig pricing rule and the rule that derives from the … Read more
We give an upper bound for the number of different basic feasible solutions generated by Dantzig’s simplex method (the simplex method with the most negative pivoting rule) for LP with bounded variables by extending the result of Kitahara and Mizuno (2010). We refine the analysis by them and improve an upper bound for a standard … Read more
In this short paper, we give an upper bound for the number of different basic feasible solutions generated by the simplex method for linear programming problems having optimal solutions. The bound is polynomial of the number of constraints, the number of variables, and the ratio between the minimum and the maximum values of all the … Read more
Kitahara and Mizuno (2010) get two upper bounds for the number of different basic feasible solutions generated by Dantzig’s simplex method. The size of the bounds highly depends on the ratio between the maximum and minimum values of all the positive elements of basic feasible solutions. In this paper, we show some relations between the … Read more
Let the least value of a function of many variables be required. If its gradient is available, then one can tell whether search directions are downhill, and first order conditions help to identify the solution. It seems in practice, however, that the vast majority of unconstrained calculations do not employ any derivatives. A view of … Read more
In this paper we devise a new version of primal simplex algorithms in which the classical iteration is decomposed two basic operations: the move and the pivot. The move operation decreases the primal objective value and the pivot operation increases the dual objective. We define the condition number of the pivot operation and present a … Read more
The simplex method is frequently the most efficient method of solving linear programming (LP) problems. This paper reviews previous attempts to parallelise the simplex method in relation to efficient serial simplex techniques and the nature of practical LP problems. For the major challenge of solving general large sparse LP problems, there has been no parallelisation … Read more
This paper presents a new primal dual simplex method and investigates the duality formation implying in classical simplex methods. We reviews classical simplex methods for linear programming problems and give a detail discussion for the relation between modern and classical algorithms. The two modified versions are present. The advantages of the new algorithms are simplicity … Read more