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Computing Methods in Optimization Problems: Papers presented by G. Arienti, A. Colonelli Daneri, M. Auslender, E. J.

By G. Arienti, A. Colonelli Daneri, M. Auslender, E. J. Beltrami, L. F. Buchanan, A. R. Stubberud, Philippe A. Clavier, R. Cosaert, E. Gottzein, A. De Maio, G. Guardabassi, A. Locatelli, S. Rinaldi, Mark Enns, H. O. Fattorini, Jean Fave, F. Caroti Ghelli, D.

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Extra info for Computing Methods in Optimization Problems: Papers presented at the 2nd International Conference on Computing Methods in Optimization Problems, San Remo, Italy, September 9–13, 1968

Example text

Another payoff to society is evident by noting the significant reduction in the standard deviation of the cost as a decreases. Here again we note the strong dependence of the optimal control systems behavior on the penalty function. Extension to More Complex Penalty Functions In this section we extend the above results to consider a broader class of penalty functions, especially with respect to the relative weighting applied to each component of the control vector,~. Table 2 presents the various functional forms of the penalty function studied.

This defines the minimum confidence limit corresponding to the X% confidence level. In the same manner, a threshhold can be found such that equally or less likely results are X% probable. This is the maximum confidence limit. A businessman can always be asked the odds he is willing to accept when gambling for high stakes. This is the confidence he wants in the results of the simulation. This confidence corresponds to a confidence limit. The businessman may be assured with the confidence he wishes to maintain that a result at least as beneficial as the confidence limit will occur.

Johnson, P. Wheatley: An Accelerated Gradient Method for Parameter Optimization with Nonlinear Constraints. Jour. Astro. Sci. 13 (1966), 166-169. : The Created Response Surface Technique for Optimizing Nonlinear Restrained Systems. Operations Res. 9 (1961), 169-184. (20) Fiacco, A. V. and G. McCormick: Programming under Nonlinear Constraints by Unconstrained Minimization. RAC Report RAC-TP-96 (1963). (21) Fiacco, A. V. and G. McCormick: Computational Algori thIn for Sequential Unconstrained Minimization Technique for Nonlinear Programming.

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