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Users of linear programming computer codes have realized the necessity of evaluating the capacity, effectiveness, and accuracy of the solutions provided by such codes. Large scale linear programming codes at most installations are assumed to be generating correct solutions without ever having been "benche-marked" by test problems with known solutions. The reason for this fail- ure to adequately test the codes is that rarely are there large problems with known solutions readily available. This paper presents a theoretical justification and an illustrative implementation of a method for generating linear programming test problems with known solutions. The method permits the generation of test problems that are of arbitrary size and have a wide range of numerical characteristics. [ABSTRACT FROM AUTHOR]

This paper is concerned with a review and examination of several existing algorithms for the zero-one programming problem. Computational experience is summarized. The machine time and storage requirements of several of the algorithms are compared over several test problems of small and intermediate size. Computer experiments still provide little hope of solving problems with over 100 variables with a reasonable amount of machine time. [ABSTRACT FROM AUTHOR]