Blurb::
Surrogate model usage mode for mesh adaptive search

Description::
The \c use_surrogate keyword is used to define how a surrogate model
(if one is provided) is to be used by \c mesh_adaptive_search.  There
are two approaches available: \c inform_search uses the surrogate to
sort list of trial points and subsequently the true function is
evaluated on the most promising points first.  Both true function and
surrogate are used interchangeably within the method.  \c optimize
forces the use of a surrogate in lieu of the true model and thus the
true function is never invoked except to construct the surrogate.
  
<em>Known Issue: When using discrete variables, there have been
sometimes significant differences in surrogate behavior observed
across computing platforms in some cases.  The cause has not yet been
fully diagnosed and is currently under investigation.  In addition,
guidance on appropriate construction and use of surrogates with
discrete variables is under development.  In the meantime, users
should therefore be aware that there is a risk of inaccurate results
when using surrogates with discrete variables.</em>

<b> Default Behavior </b>

By default, \c mesh_adaptive_search follows behaviour provided by \c
optimize option.

Examples::

The following example shows the syntax used to set \c use_surrogate.

\verbatim
method,
	mesh_adaptive_search
	model_pointer = 'SURROGATE'
	use_surrogate inform_search

model,
	id_model = 'SURROGATE'
	surrogate global
	polynomial quadratic
	dace_method_pointer = 'SAMPLING'

variables,
	continuous_design = 3
	  initial_point   -1.0    1.5   2.0
	  upper_bounds    10.0   10.0  10.0
	  lower_bounds   -10.0  -10.0 -10.0
	  descriptors      'x1'  'x2'  'x3'
	discrete_design_range = 2
	  initial_point      2     2
	  lower_bounds       1     1
	  upper_bounds       4     9
	  descriptors      'y1'   'y2'
       discrete_design_set
         real = 2
           elements_per_variable = 4 5
           elements = 1.2 2.3 3.4 4.5 1.2 3.3 4.4 5.5 7.7
           descriptors      'y3'   'y4'
         integer = 2
           elements_per_variable = 2 2
           elements = 4 7 8 9
           descriptors      'z1'   'z2'

method,
	id_method = 'SAMPLING'
	model_pointer = 'TRUTH'
	sampling
	  samples = 55

model,
	id_model = 'TRUTH'
	single
	  interface_pointer = 'TRUE_FN'

interface,
	id_interface = 'TRUE_FN'
       direct
	  analysis_driver = 'text_book'

responses,
	objective_functions = 1
	no_gradients
	no_hessians
\endverbatim

Theory::
Faq::
See_Also::
