After the
handle has been initialized (e.g.,
e04raf has been called),
e04rgf may be used to declare the objective function of the problem as a nonlinear function and define the sparsity pattern (list of nonzero elements) of its gradient. If the objective function has already been defined, it will be overwritten and its Hessian (or the Hessian of the Lagrangian) will be removed. If
e04rgf is called with no nonzeroes in the sparsity pattern,
any existing objective function is removed, no new one is added and the problem will be solved as a feasible point problem.
This objective function will typically be used for nonlinear programming problems (NLP) of the kind:
The values of the nonlinear objective function
$f\left(x\right)$ and the nonzero values of its gradient
$\frac{\partial f}{\partial {x}_{i}}$ (matching the sparsity pattern) evaluated at particular points in the decision variable space will be communicated to the NLP solver by user-supplied functions (e.g.,
objfun and
objgrd for
e04stf). See
Section 3.1 in the
E04 Chapter Introduction for more details about the NAG optimization modelling suite.
None.
If on entry
${\mathbf{ifail}}=0$ or
$\mathrm{-1}$, explanatory error messages are output on the current error message unit (as defined by
x04aaf).
Not applicable.
Background information to multithreading can be found in the
Multithreading documentation.
Internal changes have been made to this routine as follows:
- At Mark 27.1: Previously, it was not possible to modify the objective function once it was set or to edit the model once a solver had been called. These limitations have been removed and the associated error codes were removed.
For details of all known issues which have been reported for the NAG Library please refer to the
Known Issues.