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/** \page UnconstrainedObject  Declaring objective functions

Nonlinear programming problems are classified according to the availability of
derivative information.  The three categories 

<ul>
	<li> NLP0 - no available derivative information,
	<li> NLP1 - analytic first derivatives available, and
	<li> NLP2 - analytic first and second derivatives available 
</ul>	
The NLPX classes are abstract base classes for NLFX.

Listed below are the constructors for the objective function. 
<ul>
<li> \code
typedef void (*USERFCN0)(int, const ColumnVector&, real&, int&);
\endcode
In the following code fragment, we show the calling sequence for a 
trigonometric function of dimension \a n. 
\code
   trig(n, x, f, result);
\endcode
where \a x is the current point, \a f is the value of 
	the objective function, and \a result is an output argument.

<li> \code
typedef void (*USERFCN1)(int, int, const ColumnVector&, real&, 
                         ColumnVector&, int&);
\endcode

The function call for the Rosenbrock problem with analytic derivatives is 
\code
   rosen(mode, n, x, f, g, result);
\endcode
where \a g is the gradient of the objective function.

<li> \code
typedef void (*USERFCN2)(int, int, const ColumnVector&, real&, 
                         ColumnVector&, SymmetricMatrix&, int&);
\endcode
Similary, the function call for the illumination problem with analytic 
derivatives and Hessian is 
\code
   illum2(mode, n, x, f, g, H, result);
\endcode
where \a H is the Hessian of the objective function.
</ul>

<p> <a href="BoundConstrainedProblems.html">Next Section: Bound-constrained
 minimization </a> | <a href="UnconstrainedProblems.html">Back to Unconstrained minimization</a></p> 

Last revised <em> June 30, 2006</em>

*/