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Mathematical programming (MP) problems can be viewed as abstractions of real-world situations. They consist of an objective function which needs to be maximized or minimized, subject to a set of constraints which defines a feasible region. The feasible region denoted by R, is often defined by a set of linear inequalities. For "real world" problems there can be thousands of inequalities and variables. A problem with such large systems is that there are often errors in formulating the constraints which may cause the feasible region to be empty. Another problem is that many of the constraints may be redundant. We define such systems as contaminated systems of linear inequalities. This thesis develops the first method to simultaneously deal with infeasibility and redundancy. The new procedure is a probabilistic approach based on an equivalence to the set covering problem. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1997 .E44. Source: Masters Abstracts International, Volume: 37-01, page: 0284. Adviser: R. J. Caron. Thesis (M.Sc.)--University of Windsor (Canada), 1997.
El-Khatib, Halima M., "A probabilistic method for cleaning contaminated systems of linear inequalities." (1997). Electronic Theses and Dissertations. 712.