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

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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Read Online or Download 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 PDF

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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

The computer simulation can only predict more accurately and more rapidly than ~n be done by instinct alone the consequences of decisions within one1s view of the situation. The glamour newly found in forecasting by computers is attracting charlatans of many kinds. The TV weatherman, for instance, who voices the probability of rain for the next day is unwittingly guilty. Is the probability of rain due to the cloud formation he expects? Or is the probability of rain over a set of probable cloud formations, each one being expected with a certain probability?

The factor could be, for example, an interest rate. The statistics may be imaginary, provided to assess the sensitivity of the effects of a decision on input assumptions, Suppose that the interest rate is known. One may wish to endow it with statistics to measure the sensitivity of the solution to a change in interest rate. The statistics may be "statistics of statistics'" The mean and standard deviation of a Gaussian distribution may not be known and may themselves be endowed with statistics. Our lack of knowledge about the probabi lity that oil will be found in a drilling venture may best be represented by endowing the probability with statistics.

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. The magnitude of each penalty function, a, was again varied over two orders of magnitude.

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