/** \page BoundConstrainedProblems Bound-constrained minimization In this section, we describe the framework for a bound-constrained minimization problem.

- \ref BoundConstraints
- \ref BCProblem
- \ref BCFragments

\section BCProblem Creating a bound-constrained nonlinear problem Once you have mastered bound-constrained objects and setting up the objective function, it is a simple 2-step process to build a bound-constrained nonlinear problem. Let's consider the two-dimensional Rosenbrock problem with bounds on the variables: minimize \f[100(x_2 - x_{1}^2)^2 + (1 - x_1)^2 \f] subject to \f[ -2.0 \le x_1 \le 2.0 \f] \f[ -2.0 \le x_2 \le 2.0 \f] Step 1: Build your bound constraint. \code int ndim = 2; ColumnVector lower(ndim), upper(ndim); lower = -2.0; upper = 2.0; Constraint bc = new BoundConstraint(ndim, lower, upper); \endcode Step 2: Create a constrained NLF1 object. \code NLF1 rosen_problem(n,rosen,init_rosen,&bc); \endcode \section BCFragments Specifying the optimization method OPT++ contains no less than six solvers for bound-constrained optimization problems. To name a few, there are implementations of Newton's method, barrier Newton's method, interior-point methods, and direct search algorithms. We provide examples of solving the bound-constrained Rosenbrock problem with an active set strategy and a nonlinear interior-point method.

1. \ref tstbcqnewton
2. \ref tstbcnips

Next Section: Constrained minimization | Back to Solvers Page

Last revised July 13, 2006 */