On an inverse linear programming problem

A method for solving the following inverse linear programming (LP) problem is proposed. For a given LP problem and one of its feasible vectors, it is required to adjust the objective function vector as little as possible so that the given vector becomes optimal. The closeness of vectors is estimated...

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Bibliographic Details
Published in:Proceedings of the Steklov Institute of Mathematics Vol. 295; no. Suppl 1; pp. 21 - 27
Main Authors: Amirkhanova, G. A., Golikov, A. I., Evtushenko, Yu. G.
Format: Journal Article Conference Proceeding
Language:English
Published: Moscow Pleiades Publishing 01.12.2016
Springer Nature B.V
Subjects:
Linear programming
Mathematics
Mathematics and Statistics
Optimization
Quadratic equations
generalized Newton method
linear programming
duality
inverse linear programming problem
unconstrained optimization
ISSN:0081-5438, 1531-8605
Online Access:Get full text
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Summary:A method for solving the following inverse linear programming (LP) problem is proposed. For a given LP problem and one of its feasible vectors, it is required to adjust the objective function vector as little as possible so that the given vector becomes optimal. The closeness of vectors is estimated by means of the Euclidean vector norm. The inverse LP problem is reduced to a problem of unconstrained minimization for a convex piecewise quadratic function. This minimization problem is solved by means of the generalized Newton method.
Bibliography:ObjectType-Article-1
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SourceType-Conference Papers & Proceedings-1
content type line 22
ISSN:0081-5438
1531-8605
DOI:10.1134/S0081543816090030