SNOPT
make use of nonlinear
function and gradient
values. The solution
obtained will be a local
optimum (which may or
may not be a global
optimum). If some of
the gradients are unknown,
they will be estimated
by finite differences.
Infeasible problems
are treated methodically
via elastic bounds.
SNOPT allows the nonlinear
constraints to be violated
(if necessary) and minimizes
the sum of such violations.
For
large problems,
efficiency is improved
if only some of the
variables enter nonlinearly,
or if the number of
active constraints is
nearly as large as the
number of variables
(i.e., if there are
few degrees of freedom
at a solution). SNOPT
can accommodate problems
with many degrees of
freedom (perhaps one
or two thousand), but
a few hundred or less
is preferable.
