After the
handle has been initialized (e.g.,
handle_init has been called),
handle_set_nlnobj 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
handle_set_nlnobj 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
and the nonzero values of its gradient
(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
handle_solve_ipopt). See
Section 3.1 in the
E04 Chapter Introduction for more details about the
NAG optimization modelling suite.
None.
All errors and warnings have an associated numeric error code field,
errorid, stored either as a member of the thrown exception object (see
errorid), or as a member of
opt.
ifail, depending on how errors
and warnings are being handled (see
Error Handling for more details).
Not applicable.
Please see the description for the underlying computational routine in this section of the
FL Interface documentation.
Examples of the use of this method may be found in the examples for:
handle_solve_bounds_foas.