###############################################################################
#######     Definition of Run Parameters attributes (QuadModel)    ############
#####
##### . Each definition must be separated by at least a comment line
#####     (starting by #)
#####
##### . The definition must provide the name, type and default value
#####     (no default: '-' or 'N/A') in this order, one by line.
#####
##### . Only a single word is considered for the name,
#####     type and default value (extra is comment)
#####
##### . The short info, the help and the keywords must be provided in this
#####     order within "\(" and "\). For keywords, the plural part of a word
#####     can be put in parenthesis; both singular and plural will be keywords
#####
##### . The default value is automatically reported in the help.
#####
##### . In addition, the definition can have optional attributes:
#####       - ALGO_COMPATIBILITY_CHECK yes/no, the default is no. If yes, the
#####         value of this attribute can be used for comparing two sets of
#####         attributes for algorithm compatibility. This is used by the
#####         Runner.
#####
#####       - RESTART_ATTRIBUTE yes/no, the default is no. If yes, the
#####         value of this attribute can be modified during hot or warm
#####         restart. Otherwise, modifying this attribute during hot restart
#####         will trigger an error message.
#####
#####       - UNIQUE_ENTRY yes/no, the default is yes. If no, multiple
#####         values may be assigned to this parameter.
#####
###############################################################################
QUAD_MODEL_SEARCH
bool
true
\( Quad model search \)
\(

. MADS model search, using Bastien Talgorn's Sgtelib with quad models

. Argument: one boolean ('yes' or 'no')

. Disabled for more than 50 variables

. Example: QUAD_MODEL_SEARCH yes

\)
\( basic mads quad model search model_search \)
ALGO_COMPATIBILITY_CHECK yes
RESTART_ATTRIBUTE yes
###############################################################################
QUAD_MODEL_SEARCH_SIMPLE_MADS
bool
false
\( Quad model search using a simpler version of Mads \)
\(

. SimpleMads has only one poll method and no search method.

. The evaluator control is by-passed. The model is directly evaluated.

. SimpleMads use a simpler progressive barrier approach to handle constraints.

. This approach is more straightforward but maybe less efficient.

. Example: QUAD_MODEL_SEARCH_SIMPLE_MADS yes

\)
\( basic mads quad model search model_search \)
ALGO_COMPATIBILITY_CHECK yes
RESTART_ATTRIBUTE yes
###############################################################################
QUAD_MODEL_SEARCH_BOUND_REDUCTION_FACTOR
NOMAD::Double
1
\( Scale the bounds for the quad model search  \)
\(

. The quad model is built on evaluation points around a frame center. This
defines min and max bounds, from which we define a model center. The model
search is limited to tighter (can be wider with the parameter set to less than 1)
bounds by reducing the distance of the optimization bounds to the model center.
We use a reduction factor for that. If the reduction factor equals one, the min
and max bounds are used as optimization bounds for the search. The greater the
reduction factor, the tighter the bounds.

. Argument: one Double greater than 0

. Example: QUAD_MODEL_SEARCH_BOUND_REDUCTION_FACTOR 3.0

\)
\( develop mads quad model search sgtelib model_search bounds \)
ALGO_COMPATIBILITY_CHECK yes
RESTART_ATTRIBUTE yes
################################################################################
QUAD_MODEL_DISPLAY
std::string
-
\( Display of a model \)
\(
. Control the display of the quad model search and quad model optimization.
  These details are only shown if DISPLAY_DEGREE is FULL (3) or more.

. Arguments: a string containing one or several of the following letters

. \"S\": General information on the model search or optimization

. \"F\": Details of the filter step

. \"O\": Details of the models optimization

. \"P\": Details of the projection

. \"U\": Details of the model update

. \"I\": Advancement of the model optimization

. \"X\": Display of all of the model evaluations

. Example: QUAD_MODEL_DISPLAY SPF # display the general information on the search
                                       and on the filter and projection steps
\)
\( developer advanced model quad sgtelib \)
ALGO_COMPATIBILITY_CHECK no
RESTART_ATTRIBUTE no
################################################################################
QUAD_MODEL_OPTIMIZATION
bool
false
\( Quad model stand alone optimization for constrained and unconstrained pbs \)
\(

. Quadratic model optimization for constrained and unconstrained
  optimization.
  
. LH_EVAL can be used to sample points and evaluate them before running
  quad model optimization.

. Argument: bool

. Stand alone quadratic model optimization will deactivate any optimization
  strategy.

. Example: QUAD_MODEL_OPTIMIZATION true

\)
\( advanced sgtelib quadratic quad optimization simplex \)
ALGO_COMPATIBILITY_CHECK yes
RESTART_ATTRIBUTE no
################################################################################
QUAD_MODEL_SEARCH_BOX_FACTOR
NOMAD::Double
4.0
\( Quadratic model search point selection factor \)
\(
. Quadratic model search point selection factor

. This parameter is used to select points to build the quadratic model for
  the search method

. The max, min and average of trial points coordinates are used to identify a
  box. This box contains all trial points to sort.
  
. The box is enlarged by this factor. Cache evaluation points inside this box
  are selected to build the quadratic model for search.

. Arguments: one strictly positive real

. Example: QUAD_MODEL_SEARCH_BOX_FACTOR 1.0
\)
\( developer quadratic model search radius \)
ALGO_COMPATIBILITY_CHECK yes
RESTART_ATTRIBUTE yes
################################################################################
QUAD_MODEL_BOX_FACTOR
NOMAD::Double
4.0
\( Quadratic model points selection box factor \)
\(

. This parameter is used to select points to build the quadratic model.

. The max, min and average of trial points coordinates are used to identify a
  box.
  
. The box is enlarged by this factor. Cache evaluation points inside this box
  are selected to build the quadratic model.

. Arguments: one strictly positive real

. Example: QUAD_MODEL_BOX_FACTOR 1.0
\)
\( developer quadratic model sort search \)
ALGO_COMPATIBILITY_CHECK yes
RESTART_ATTRIBUTE yes
################################################################################
QUAD_MODEL_SEARCH_FORCE_EB
bool
false
\( Quadratic model search optimization using extreme barrier \)
\(

. By default (flag=false) quadratic model search optimization problem transforms
  all constraints into progressive barrier constraints.
  
. Handling constraints with the progressive barrier is more costly (time) than
  with the extreme barrier because updating the progressive barrier is more
  complex.

. On costly blackbox optimization problem the extra time for using the progressive
  barrier during the quadratic model search optimization is minimal. But it can
  be more important on simple analytical blacbox optimization problems with many
  quad model searches.
  
. Forcing EB constraints for quad model search is faster but can result in less
  successful trial points.
  
. Arguments: boolean

. Example: QUAD_MODEL_FORCE_EB true

\)
\( developer quadratic model search \)
ALGO_COMPATIBILITY_CHECK yes
RESTART_ATTRIBUTE yes
################################################################################
