Hello, In this program I’m trying to maximize portfolio skewness under constraints. Nevertheless , I have one more problem. In my case, I have one linear constraint and one Nonlinear constraint (quartic constraint about portfolio kurtosis which is less than a scalar). As I understand while reading chapter 14 about Nonlinear optimization examples, I must use NLPQN instead of NLPNRA! IS that sufficient? In the other hand, I still think about the mining of “optn”! and if x0 must take always only two initial values? Finally, I want to state decision variables wi as positive, I question whether it been subject to a further constraint. Sorry for those lots of questions. Thank you in advance! proc iml; M3 = {32 -7 -7 -8, -7 -8 -8 17}; start skpf(p) global(M3); w = p`; /* w is column vector */ return w` * M3 * (w@w); finish; /* specify linear constraints */ con = { 0 0 . ., /* min w[i] */ 1 1 . ., /* max w[i] */ 1 1 0 1}; /* sum(w) = 1 */ x0 = {0.5 0.5}; optn = {1 /* maximize objective function */ 1 }; /* summarize iteration history */ call nlpnra(rc, xOpt, "skpf", x0, optn, con); maxVal = skpf(xOpt); print rc, xOpt maxVal;
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