• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Advanced Search Include Citations

Tools

Sorted by:
Try your query at:
Semantic Scholar Scholar Academic
Google Bing DBLP
Results 1 - 10 of 142,063
Next 10 →

Solving Fuzzy Linear Programming Problem as Multi Objective Linear Programming Problem

by unknown authors
"... 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. ..."
Abstract - Add to MetaCart
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

by Nimrod Megiddo
"... . 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 ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
. 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

algorithm for linear programming problems

by N. Ploskas, N. Samaras, A. Sifaleras
"... 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. ..."
Abstract - Add to MetaCart
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

for linear programming problems under uncertainty

by Nathan Huntley, O Quiñones, Keivan Shariatmadar, Erik Quaeghebeur, Gert De Cooman, Etienne Kerre
"... 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 ..."
Abstract - Add to MetaCart
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

Algorithms for Linear Programming Problems

by Dianne P. O’leary
"... c○2008 ..."
Abstract - Add to MetaCart
Abstract not found

Linear Programming Problems for Frontier Estimation

by Guillaume Bouchard, Stéphane Girard, Anatoli Iouditski, Alexander Nazin - 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 ..."
Abstract - Cited by 5 (1 self) - Add to MetaCart
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

Large-Scale Linear Programming Problems

by Ananth R. Madabushi, Dr. Sheldon, H. Jacobson, Dr. John, E. Kobza, Ananth R. Madabushi, Dr. Hanif, D. Sherali , 1997
"... 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, and ..."
Abstract - Add to MetaCart
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

Interval Linear System and Linear Programming Problem

by Hassan Mishmast Nehi , Mohammad Javad Lalehchini , 2008
"... 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. ..."
Abstract - Add to MetaCart
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.

Robust solutions of Linear Programming problems contaminated with uncertain data

by Aharon Ben-tal, Arkadi Nemirovski - 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 well-known NETLIB collection. We then apply the Robust Optimization methodology (Ben-Tal and Nemirovski [1-3]; El Ghao ..."
Abstract - Cited by 175 (6 self) - Add to MetaCart
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

Exact solutions to linear programming problems

by William Cook, Sanjeeb Dash, Daniel G. Espinoza - Operations Research Letters , 2007
"... 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 ra ..."
Abstract - Cited by 24 (7 self) - Add to MetaCart
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.
Next 10 →
Results 1 - 10 of 142,063
Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University