Abstract — This paper proposes the method to the solution of fuzzy linear programming problem with the help of multi objective constrained linear programming problem when the constraint matrix and the cost coefficients are fuzzy in nature and it is also explained with an illustrative example.

. This paper studies the complexity of some approximate solutions of linear programming problems with real coefficients. 1. Introduction The general linear programming problem is to maximize a linear function over a set defined by linear inequalities and equations. There are many equivalent ways

We present a software implementation of the methods for solving linear programming problems under uncertainty from previous work. Uncertainties about constraint parameters can be expressed as intervals or trapezoidal possibility distributions. The software computes the solutions for the optimality

Abstract—The simplex method is perhaps the most widely used method for solving linear programming (LP) problems. The computation time of simplex type algorithms depends on the basis inverse that occurs in each iteration. Parallelizing simplex type algorithms is one of the most challenging problems

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of the linear combination are then computed by solving a linear programming problem. In the general case, the solution of the optimization problem is sparse, that is, only a few coefficients are non zero. The corresponding points play the role of support vectors in the statistical learning theory. The L 1 error

This research effort focuses on large-scale linear programming problems that arise in the context of solving various problems such as discrete linear or polynomial, and continuous nonlinear, nonconvex programming problems, using linearization and branch-and-cut algorithms for the discrete case

Abstract In this paper we discuss on solving the interval linear system and interval linear programming problems. In this model we let the coefficient matrix and the right vector hands and the cost coefficient are interval.

Optimal solutions of Linear Programming problems may become severely infeasible if the nominal data is slightly perturbed. We demonstrate this phenomenon by studying 90 LPs from the well-known NETLIB collection. We then apply the Robust Optimization methodology (Ben-Tal and Nemirovski [1-3]; El

The use of floating-point calculations limits the accuracy of solutions obtained by standard LP software. We present a simplex-based algorithm that returns exact rational solutions, taking advantage of the speed of floating-point calculations and attempting to minimize the operations performed in rational arithmetic. Extensive computational results are presented.