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Solving Fuzzy Linear Programming Problem as Multi Objective Linear Programming Problem
"... 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. ..."
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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.
On Solving the Linear Programming Problem Approximately
"... . 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 to ..."
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. 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
for linear programming problems under uncertainty
"... 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 c ..."
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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
algorithm for linear programming problems
"... 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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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
Linear Programming Problems for Frontier Estimation
 IN SECOND INTERNATIONAL CONTROL CONFERENCE, MOSCOU, RUSSIE, JUIN
, 2003
"... We propose new estimates for the frontier of a set of points. They are de ned as kernel estimates covering all the points and whose associated support is of smallest surface. The estimates are written as linear combinations of kernel functions applied to the points of the sample. The coefficients of ..."
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Cited by 5 (1 self)
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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
LargeScale Linear Programming Problems
, 1997
"... This research effort focuses on largescale 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 branchandcut algorithms for the discrete case, and ..."
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This research effort focuses on largescale 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 branchandcut algorithms for the discrete case
Robust solutions of Linear Programming problems contaminated with uncertain data
 Mathematical Programming
, 2000
"... 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 wellknown NETLIB collection. We then apply the Robust Optimization methodology (BenTal and Nemirovski [13]; El Ghao ..."
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Cited by 175 (7 self)
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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 wellknown NETLIB collection. We then apply the Robust Optimization methodology (BenTal and Nemirovski [13]; El
Reductionbyprojection Method for Linear Programming Problems
"... Abstract: This paper defines the projection of algebic systems, and studies the projecting algorithm for linear systems. As its application, a new method is given to solve linear programming problems, which is called reductionbyprojection method. For many problems, especially when the problems hav ..."
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Abstract: This paper defines the projection of algebic systems, and studies the projecting algorithm for linear systems. As its application, a new method is given to solve linear programming problems, which is called reductionbyprojection method. For many problems, especially when the problems
Exact solutions to linear programming problems
 Operations Research Letters
, 2007
"... The use of floatingpoint calculations limits the accuracy of solutions obtained by standard LP software. We present a simplexbased algorithm that returns exact rational solutions, taking advantage of the speed of floatingpoint calculations and attempting to minimize the operations performed in ra ..."
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Cited by 24 (7 self)
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The use of floatingpoint calculations limits the accuracy of solutions obtained by standard LP software. We present a simplexbased algorithm that returns exact rational solutions, taking advantage of the speed of floatingpoint calculations and attempting to minimize the operations performed in rational arithmetic. Extensive computational results are presented.
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