Language Reference

CALL NLPNRR (rc, xr, "fun", x0 <, opt> <, blc> <, tc> <, par> <, "ptit"> <, "grd"> <, "hes"> ) ;

This subroutine is supported only by the IML procedure.

The NLPNRR subroutine uses a Newton-Raphson ridge method to compute an optimum value of a function.

See the section Nonlinear Optimization and Related Subroutines for a listing of all NLP subroutines. See Chapter 16 for a description of the arguments of NLP subroutines.

The NLPNRR algorithm uses a pure Newton step when both the Hessian is positive definite and the Newton step successfully reduces the value of the objective function. Otherwise, a multiple of the identity matrix is added to the Hessian matrix.

The subroutine uses the gradient and the Hessian matrix . It requires continuous first- and second-order derivatives of the objective function inside the feasible region.

Note that using only function calls to compute finite difference approximations for second-order derivatives can be computationally very expensive and can contain significant rounding errors. If you use the "grd" input argument to specify a module that computes first-order derivatives analytically, you can reduce drastically the computation time for numerical second-order derivatives. The computation of the finite difference approximation for the Hessian matrix generally uses only n calls of the module that specifies the gradient.

The NLPNRR method performs well for small- to medium-sized problems, and it does not need many function, gradient, and Hessian calls. However, if the gradient is not specified analytically by using the "grd" module argument, or if the computation of the Hessian module specified with the "hes" argument is computationally expensive, one of the (dual) quasi-Newton or conjugate gradient algorithms might be more efficient.

In addition to the standard iteration history, the NLPNRR subroutine prints the following information:

The following statements invoke the NLPNRR subroutine to solve the constrained Betts optimization problem (see the section Constrained Betts Function). The iteration history follows.

start F_BETTS(x);
   f = .01 * x[1] * x[1] + x[2] * x[2] - 100;
   return(f);
finish F_BETTS;

con = {  2 -50  .   .,
        50  50  .   .,
        10  -1  1  10};
x = {-1 -1};
opt = {0 2};
call nlpnrr(rc, xres, "F_BETTS", x, opt, con);