You need to modify the line: call quad(val, "Func", yinterval); /* evaluate integral on [0, 1] */ val will not be replaced by the quad call. do call quad(result, "Func", yinterval); /* evaluate integral on [0, 1] */ val = result; ... View more

What operations do you need to do on the tree? Do you need to be able to add/delete nodes or prune branches? How is the tree to be constructed? ... View more

For a 4th degree poynomial there will be generally 4 complex solutions. Looks like the polyroot() function  is what you need, check the IML documentation. I suggest you first compute z = polyroot( {1.2 1.2 1.2 1.2 -10) to get the imaginary roots, then  since z = 1 + x, x = z-1, just subtract 1 from the first column of z to get x, i. e. proc iml; a = {1.2 1.2 1.2 1.2 -10}; z = polyroot(a); x = z - {1 0,1 0,1 0,1 0}; print x; ... View more

The other default values seem to be in line with the 9.1 doc, so there could be an error in the documentation regarding that parameter. It might be helpful if you shared your program, to verify your setup for the optimization. ... View more

There are many different ways tolook at computational efficiency. The Nelder-Mead simplex algorithm, because it does not use gradients directly, may require more objective function evaluations than some other techniques such as the conguate gradient method. However, the computation of gradients and Hessians can sometimes be very cpu-intensive themselves, so the least cpu-intensive method generally depends on the problem constraints and objective function. There are also several ways to estimate the accuracy of the results. Look at the SAS/IML doc under Nonlinear Optimization Examples->Details->Termination Criteria. IML provides several options for terminating the optimization process for NLPNMS, based on the relative or absolute change in value of either the objective function or the parameters between iterations of the algorithm, and that should give you some estimate of the accuracy of the results. Obviously, the tighter the termination criteria, the more iterations will be required. Since your input data or model iteslf is probably not exact in the first place, requiring extreme accuracy may not be justified and can lead to excessive run times or numerical problems. Keep in mind also that all the nlp methods will converge to a local optimum, so unless your objective function is complex it is possible that your answer might not always be the global one. ... View more

Could you give us or point us to a complete mathematical description of your problem? ... View more