Chinneck J., Finding the Most Useful Subset of Constraints for Analysis in an Infeasible Linear Program, Technical Report SCE-93-07, Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada, 1993.  Home/Search   Document Not in Database   Summary   Related Articles

....a localization of the modeling error inconsistency. Thus, this approach provides a framework with which not only to isolate the infeasibility, but also to diagnose the infeasibility. To date, this approach had only been explored in the theoretical sense. Recently, Chinneck (e.g. 13] 10] [11]) has developed a set of software tools to bring I IS isolation into practical use in LP infeasibility analysis. The goal of these algorithms is to identify a small cardinality I ISs, the idea being that the smaller the constraint set the infeasibility is isolated to the easier the actual ....

....IS on all problems, and taking fewest average number of pivots on 6 of 9 problems. It also finds the smallest average I IS on 4 of 6 problems where distinction is made. We also compare (Table 5. 5) the information provided by the I IS cover to that obtained from I IS isolation via MINOS(I IS) from [11]) Many of the problems in this test bed were created by taking a feasible LP instance and modifying a single bound or constraint until the problem was infeasible. Four of the problems in the test bed we know to be infeasible in original form BGDBG1, BGPRTR, GREENBEA, and MONDOU2. Of these ....

Chinneck J., Finding the Most Useful Subset of Constraints for Analysis in an Infeasible Linear Program, Technical Report SCE-93-07, Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada, 1993.

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