Title : ( Laws of Large Numbers for Random Linear Programs under Dependent Models )
Authors: Sara Jomhoori , Vahid Fakoor , Hassan Ali Azarnoosh ,
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Abstract
The computational solution of large scale linear programming problems contains various difficulties. One of the most difficult things is to ensure numerical stability. We have another difficulty of different nature, namely the original data we are using also contains errors. In this paper we show that the effect of the random errors in the original data has a diminishing tendency for the optimal value as the number of constraints and the number of variables increase. We obtain laws of large numbers for random linear programs to the case of dependent random variables.
Keywords
, laws of large numbers, m-dependent random variables, random linear programs, strongly mixing random variables
@article{paperid:1006791,
author = {Jomhoori, Sara and Fakoor, Vahid and Azarnoosh, Hassan Ali},
title = {Laws of Large Numbers for Random Linear Programs under Dependent Models},
journal = {Far East Journal of Theoretical Statistics},
year = {2007},
volume = {23},
number = {1},
month = {November},
issn = {0972-0863},
pages = {75--85},
numpages = {10},
keywords = {laws of large numbers; m-dependent random variables; random
linear programs; strongly mixing random variables},
}
%0 Journal Article
%T Laws of Large Numbers for Random Linear Programs under Dependent Models
%A Jomhoori, Sara
%A Fakoor, Vahid
%A Azarnoosh, Hassan Ali
%J Far East Journal of Theoretical Statistics
%@ 0972-0863
%D 2007
