Add to ORKGWe analyze invariance of the conclusion of optimality for the linearprogramming problem under scalings (linear, affine, . . . ) of variousproblem parameters such as: the coefficients of the objective function,the coefficients of the constraint vector, the coefficients of one or morerows (columns) of the constraint matrix. Measurement theory conceptsplay a central role in our presentation and we explain why suchapproach is a natural one.Keywords: Sensitivity Analysis, Scaling, Measuremen
Tolerance sensitivity analysis was conceived in 1980 as a pragmatic approach to effectively characte...
Postoptimality or sensitivity analysis are well-developed subjects in almost all branches of mathema...
Abstract- Linear programming is the name of a branch of applied mathematics that deals with solving ...
Matrix scaling is an operation in which the rows and columns of a matrix are multiplied by positive ...
Sensitivity analysis is used to quantify the impact of changes in the initial data of linear program...
In his recent work, Dadush et al. introduced the condition number κ for constraint matrices of linea...
Linear programming (LP) is one of the great successes to emerge from operations research and managem...
Sensitivity analysis in linear programming studies the stability of optimal solutions and the optima...
This paper takes a fresh look at sensitivity analysis in linear programming. We propose a merged app...
Parametric Linear Programming is a development model of sensitivity analysis in which the inputs coe...
Following the breakthrough work of Tardos (Oper. Res. '86) in the bit-complexity model, Vavasis and ...
Following the breakthrough work of Tardos in the bit-complexity model, Vavasis and Ye gave the first...
Parametric linear programming is the study of how optimal properties depend on data parametrizations...
Following the breakthrough work of Tardos in the bit-complexity model, Vavasis and Ye gave the firs...
An algorithm based on a special duality concept is used to solve an integer linear programming probl...
Tolerance sensitivity analysis was conceived in 1980 as a pragmatic approach to effectively characte...
Postoptimality or sensitivity analysis are well-developed subjects in almost all branches of mathema...
Abstract- Linear programming is the name of a branch of applied mathematics that deals with solving ...
Matrix scaling is an operation in which the rows and columns of a matrix are multiplied by positive ...
Sensitivity analysis is used to quantify the impact of changes in the initial data of linear program...
In his recent work, Dadush et al. introduced the condition number κ for constraint matrices of linea...
Linear programming (LP) is one of the great successes to emerge from operations research and managem...
Sensitivity analysis in linear programming studies the stability of optimal solutions and the optima...
This paper takes a fresh look at sensitivity analysis in linear programming. We propose a merged app...
Parametric Linear Programming is a development model of sensitivity analysis in which the inputs coe...
Following the breakthrough work of Tardos (Oper. Res. '86) in the bit-complexity model, Vavasis and ...
Following the breakthrough work of Tardos in the bit-complexity model, Vavasis and Ye gave the first...
Parametric linear programming is the study of how optimal properties depend on data parametrizations...
Following the breakthrough work of Tardos in the bit-complexity model, Vavasis and Ye gave the firs...
An algorithm based on a special duality concept is used to solve an integer linear programming probl...
Tolerance sensitivity analysis was conceived in 1980 as a pragmatic approach to effectively characte...
Postoptimality or sensitivity analysis are well-developed subjects in almost all branches of mathema...
Abstract- Linear programming is the name of a branch of applied mathematics that deals with solving ...