In this section, we present the constructors for an objective function, supply a prototype function evaluator, and provide examples for solving the unconstrained minimization problem.
minimize
Representing the Rosenbrock problem as an NLF1 requires an user-supplied function to evaluate the problem and construction of an NLF1.
Step 1: Write a function that evaluates the Rosenbrock problem and gradient.
void rosen(int mode, int n, const ColumnVector& x, double& fx,
ColumnVector& g, int& result)
{ // Rosenbrock's function
double f1, f2, x1, x2;
if (n != 2) return;
x1 = x(1);
x2 = x(2);
f1 = (x2 - x1 * x1);
f2 = 1. - x1;
if (mode & NLPFunction) {
fx = 100.* f1*f1 + f2*f2;
}
if (mode & NLPGradient) {
g(1) = -400.*f1*x1 - 2.*f2;
g(2) = 200.*f1;
}
result = NLPFunction & NLPGradient;
}Step 2: Create an NLF1 object.
NLF1 rosen_problem(n,rosen,init_rosen);
Specifying the optimization method
There are several algorithms in OPT++ to solve unconstrained problems. We provide examples of solving the Rosenbrock problem with a conjugate gradient and Quasi-Newton method.
Next Section: Bound-constrained Minimization | Back to Solvers Page
Last revised February 8, 2002
