By Darwin Klingman (auth.), Prof. John M. Mulvey (eds.)
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Additional info for Evaluating Mathematical Programming Techniques: Proceedings of a Conference Held at the National Bureau of Standards Boulder, Colorado January 5–6, 1981
The coefficient for the generated dummy changed signs and re- mained significant. The residual plots presented in Figures 4, 5, and 6 show that the model assumptions are satisfied. The results of the number of iterations are very similar and are presented in the Appendix. Conclusions Since this study was limited to five real problems that were small by today's standards, broad sweeping conclusions are not justified. Several conclusions and conjectures will be made. First, the "more random" the problem the more difficult it is to solve.
G. Bradley, G. Brown, and G. Graves, "Design and Implementation of Large-Scale Primal Transshipment Algorithms," Management Science 24, 1 (1977) 1-34. 3. J. C. P. Bus, "A Proposal for the Classification and Documentation of Test Problems in the Field of Nonlinear Programming," Report No. NN 9/77, Stichting Mathematisch Centrum 2 e Boerhaavestraat 49 Amsterdam 1005, HOLAND, (1977). 23 4. J. Burruss, J. Elam, and D. Klingman, "NETGEN-II: User's Manual", Research Report, Center for Cybernetic Studies, The University of Texas at Austin, (1980).
3) Analysis of the Results Table 2 contains the mean and standard deviation of each of the five replications for the number of iterations, the central processor time, and the. number of bumps at optimality. ) In three cells, all the problems generated were infeasible. , small feasible region) so that slight perturbations "trick" the solver into thinking the problem was infeasible. , the optimizer could not solve them due to numerical problems). In previous work, Layman and O'Neill have shown that the solver declared a problem with an optimal solution to be infeasible .