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Linear Programming with Estimatable Parameters by Jati K. Sengupta is a Mathematics article available to read on EtoBox.

What is Linear Programming with Estimatable Parameters about?

In a standard linear programming (LP) problem one solves for an optimal decision vector x, given the set θ = (A, b, c) of parameters. However in most applied problems the parameters in the set θ are unknown constants and therefore not given, but sample observations are usually available. Thus, in production problems, input -output data are usually available for a crosssection sample of firms, each producing multiple outputs by using multiple inputs. In such cases we have the problem of estimating the input coefficients which are the unknown parameters. Most frequently, two types of method are applied in such LP problems with unknown but estimatable parameters. One is the approach of stochastic programming with recourse, 1,2 where one computes the first-stage optimal solution vector x on the basis of prior information on θ, then adjoins to the original objective function of the LP problem suitably defined expected penalty costs to reflect the deviation of the first stage optimal solution from the correct optimal solution. This two-stage sequential solution may, however, be difficult to apply empirically, owing to inadequate knowledge of the prior information and the difficulty of de

Who reads Linear Programming with Estimatable Parameters?

Author

Jati K. Sengupta

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 8755-0024)

Published

1997

Language

EN

Field

Mathematics (Social Sciences)