search for: nminimize

...2,beta) points. Some adjacency compromise is commonly required, achieved by searching a more precise OC curve with respect to one of the points. I?m using Mathematica 6 but it is a Trial, so I would like use R intead (or better, I need it)! To exemplify, In Mathematica I call the function using NMinimize passing the restriction and parameters: /* function name "findOpt" and parameters... */ restriction = (1 - alpha) <= CDF[BinomialDistribution[sample_n, p1], c] && betha >= CDF[BinomialDistribution[sample_n, p2], c] && 0 < alpha < alphamax &&...

...2,beta) points. Some adjacency compromise is commonly required, achieved by searching a more precise OC curve with respect to one of the points. I?m using Mathematica 6 but it is a Trial, so I would like use R intead (or better, I need it)! To exemplify, In Mathematica I call the function using NMinimize passing the restriction and parameters: /* function name "findOpt" and parameters... */ restriction = (1 - alpha) <= CDF[BinomialDistribution[sample_n, p1], c] && betha >= CDF[BinomialDistribution[sample_n, p2], c] && 0 < alpha < alphamax &&...

...mple_n, p2], c] && 0 < alpha < alphamax && 0 < betha < bethamax && 1 < sample_n <= lot_Size && 0 <= c < amostra && p1 < p2 < p2max ; fcost = sample_n/lot_Size; result = NMinimize[{fcost, restriction}, {sample_n, c, alpha, betha, p2max}, Method -> "NelderMead", AccuracyGoal -> 10]; example: findOpt[0.005, 1000, 0.05, 0.05, 0.04] ==> and I got the return of values of; "n", "c", alpha and betha, computed. {0.51457...

Hi, I need to solve an optimization problem in R having linear objective function and quadratic constraints(number of variables is around 80). What are the possible choices to do this in R. optim() function only allows box constrained problems. Is it possible in nlm()? Or please tell me if there is any other routine. Thanks Amit