.. _variables:
"""""""""
variables
"""""""""
Specifies the parameter set to be iterated by a particular method.
**Topics**
block
.. toctree::
:hidden:
:maxdepth: 1
variables-id_variables
variables-active
variables-mixed
variables-relaxed
variables-continuous_design
variables-discrete_design_range
variables-discrete_design_set
variables-normal_uncertain
variables-lognormal_uncertain
variables-uniform_uncertain
variables-loguniform_uncertain
variables-triangular_uncertain
variables-exponential_uncertain
variables-beta_uncertain
variables-gamma_uncertain
variables-gumbel_uncertain
variables-frechet_uncertain
variables-weibull_uncertain
variables-histogram_bin_uncertain
variables-poisson_uncertain
variables-binomial_uncertain
variables-negative_binomial_uncertain
variables-geometric_uncertain
variables-hypergeometric_uncertain
variables-histogram_point_uncertain
variables-uncertain_correlation_matrix
variables-continuous_interval_uncertain
variables-discrete_interval_uncertain
variables-discrete_uncertain_set
variables-continuous_state
variables-discrete_state_range
variables-discrete_state_set
variables-linear_inequality_constraint_matrix
variables-linear_inequality_lower_bounds
variables-linear_inequality_upper_bounds
variables-linear_inequality_scale_types
variables-linear_inequality_scales
variables-linear_equality_constraint_matrix
variables-linear_equality_targets
variables-linear_equality_scale_types
variables-linear_equality_scales
**Specification**
- *Alias:* None
- *Arguments:* None
**Child Keywords:**
+-------------------------+--------------------+-----------------------------------------+-----------------------------------------------+
| Required/Optional | Description of | Dakota Keyword | Dakota Keyword Description |
| | Group | | |
+=========================+====================+=========================================+===============================================+
| Optional | `id_variables`__ | Name the variables block; helpful when there |
| | | are multiple |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `active`__ | Set the active variables view a method will |
| | | see |
+-------------------------+--------------------+-----------------------------------------+-----------------------------------------------+
| Optional (Choose One) | Variable Domain | `mixed`__ | Maintain continuous/discrete variable |
| | | | distinction |
| | +-----------------------------------------+-----------------------------------------------+
| | | `relaxed`__ | Allow treatment of discrete variables as |
| | | | continuous |
+-------------------------+--------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `continuous_design`__ | Design variable - continuous |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `discrete_design_range`__ | Design variable - discrete range-valued |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `discrete_design_set`__ | Design variable - discrete set-valued |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `normal_uncertain`__ | Aleatory uncertain variable - normal |
| | | (Gaussian) |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `lognormal_uncertain`__ | Aleatory uncertain variable - lognormal |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `uniform_uncertain`__ | Aleatory uncertain variable - uniform |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `loguniform_uncertain`__ | Aleatory uncertain variable - loguniform |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `triangular_uncertain`__ | Aleatory uncertain variable - triangular |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `exponential_uncertain`__ | Aleatory uncertain variable - exponential |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `beta_uncertain`__ | Aleatory uncertain variable - beta |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `gamma_uncertain`__ | Aleatory uncertain variable - gamma |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `gumbel_uncertain`__ | Aleatory uncertain variable - gumbel |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `frechet_uncertain`__ | Aleatory uncertain variable - Frechet |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `weibull_uncertain`__ | Aleatory uncertain variable - Weibull |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `histogram_bin_uncertain`__ | Aleatory uncertain variable - continuous |
| | | histogram |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `poisson_uncertain`__ | Aleatory uncertain discrete variable - |
| | | Poisson |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `binomial_uncertain`__ | Aleatory uncertain discrete variable - |
| | | binomial |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `negative_binomial_uncertain`__ | Aleatory uncertain discrete variable - |
| | | negative binomial |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `geometric_uncertain`__ | Aleatory uncertain discrete variable - |
| | | geometric |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `hypergeometric_uncertain`__ | Aleatory uncertain discrete variable - |
| | | hypergeometric |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `histogram_point_uncertain`__ | Aleatory uncertain variable - discrete |
| | | histogram |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `uncertain_correlation_matrix`__ | Correlation among aleatory uncertain |
| | | variables |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `continuous_interval_uncertain`__ | Epistemic uncertain variable - values from |
| | | one or more continuous intervals |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `discrete_interval_uncertain`__ | Epistemic uncertain variable - values from |
| | | one or more discrete intervals |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `discrete_uncertain_set`__ | Epistemic uncertain variable - discrete |
| | | set-valued |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `continuous_state`__ | State variable - continuous |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `discrete_state_range`__ | State variables - discrete range-valued |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `discrete_state_set`__ | State variable - discrete set-valued |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `linear_inequality_constraint_matrix`__ | Define coefficients of the linear inequality |
| | | constraints |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `linear_inequality_lower_bounds`__ | Define lower bounds for the linear inequality |
| | | constraint |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `linear_inequality_upper_bounds`__ | Define upper bounds for the linear inequality |
| | | constraint |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `linear_inequality_scale_types`__ | How to scale each linear inequality |
| | | constraint |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `linear_inequality_scales`__ | Characteristic values to scale linear |
| | | inequalities |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `linear_equality_constraint_matrix`__ | Define coefficients of the linear equalities |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `linear_equality_targets`__ | Define target values for the linear equality |
| | | constraints |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `linear_equality_scale_types`__ | How to scale each linear equality constraint |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
| Optional | `linear_equality_scales`__ | Characteristic values to scale linear |
| | | equalities |
+----------------------------------------------+-----------------------------------------+-----------------------------------------------+
.. __: variables-id_variables.html
__ variables-active.html
__ variables-mixed.html
__ variables-relaxed.html
__ variables-continuous_design.html
__ variables-discrete_design_range.html
__ variables-discrete_design_set.html
__ variables-normal_uncertain.html
__ variables-lognormal_uncertain.html
__ variables-uniform_uncertain.html
__ variables-loguniform_uncertain.html
__ variables-triangular_uncertain.html
__ variables-exponential_uncertain.html
__ variables-beta_uncertain.html
__ variables-gamma_uncertain.html
__ variables-gumbel_uncertain.html
__ variables-frechet_uncertain.html
__ variables-weibull_uncertain.html
__ variables-histogram_bin_uncertain.html
__ variables-poisson_uncertain.html
__ variables-binomial_uncertain.html
__ variables-negative_binomial_uncertain.html
__ variables-geometric_uncertain.html
__ variables-hypergeometric_uncertain.html
__ variables-histogram_point_uncertain.html
__ variables-uncertain_correlation_matrix.html
__ variables-continuous_interval_uncertain.html
__ variables-discrete_interval_uncertain.html
__ variables-discrete_uncertain_set.html
__ variables-continuous_state.html
__ variables-discrete_state_range.html
__ variables-discrete_state_set.html
__ variables-linear_inequality_constraint_matrix.html
__ variables-linear_inequality_lower_bounds.html
__ variables-linear_inequality_upper_bounds.html
__ variables-linear_inequality_scale_types.html
__ variables-linear_inequality_scales.html
__ variables-linear_equality_constraint_matrix.html
__ variables-linear_equality_targets.html
__ variables-linear_equality_scale_types.html
__ variables-linear_equality_scales.html
**Description**
The ``variables`` specification in a Dakota input file specifies the
parameter set to be iterated by a particular method. In the case of
- An optimization study: These variables are adjusted in order to locate an optimal design.
- Parameter studies/sensitivity analysis/design of experiments: These parameters are perturbed to explore the parameter space.
- Uncertainty analysis: The variables are associated with distribution/interval characterizations which are used to compute corresponding distribution/interval characterizations for response functions.
To accommodate these different studies, Dakota supports different:
- Variable types: design
- aleatory uncertain
- epistemic uncertain
- state
- Variable domains: continuous
- discrete
- discrete range
- discrete integer set
- discrete string set
- discrete real set
See the variables :ref:`variables:overview` for another summary of the
available variables by type and domain.
*Variable Types*
- *Design Variables:* Design variables are those variables which are modified for the purposes of seeking an optimal design.
- The most common type of design variables encountered in engineering
applications are of the continuous type. These variables may assume
any real value within their bounds.
- All but a handful of the optimization algorithms in
Dakota support continuous design variables exclusively.
- *Aleatory Uncertain Variables:* Aleatory uncertainty is also known as inherent variability, irreducible uncertainty, or randomness.
- Aleatory uncertainty is predominantly characterized using probability theory. This is the only option implemented in Dakota.
- *Epistemic Uncertain Variables:* Epistemic uncertainty is uncertainty due to lack of knowledge.
- In Dakota, epistemic uncertainty is assessed by interval analysis or the Dempster-Shafer theory of evidence
- Continuous or discrete interval or set-valued variables are used to define set-valued probabilities or basic probabiliy assignments (BPA) which define a belief structure.
- Note that epistemic uncertainty can also be modeled with probability density functions (as done with aleatory uncertainty). Dakota does not support this capability.
- *State Variables:* State variables consist of "other" variables which are to be mapped through
the simulation interface, in that they are not to be used for design and
they are not modeled as being uncertain.
- State variables provide a convenient mechanism for managing additional
model parameterizations such as mesh density, simulation convergence
tolerances, and time step controls.
- Only parameter studies and design of experiments methods will iterate
on state variables.
- The ``initial_value`` is used as the only value
for the state variable for all other methods, unless ``active``
``state`` is invoked.
- See more details in :ref:`variables:state`.
*Variable Domains*
Continuous variables are typically defined by bounds. Discrete
variables can be defined in one of three ways, which are discussed on
in :ref:`variables:design:ddv`.
.. _variables-ordering-of-variables:
*Ordering of Variables*
The ordering of variables is important, and a consistent ordering is
employed throughout the Dakota software. The ordering is shown in
dakota.input.summary (and in the hierarchical order of this reference
manual) and can be summarized as:
1. design
a. continuous
b. discrete integer
c. discrete string
d. discrete real
2. aleatory uncertain
a. continuous
b. discrete integer
c. discrete string
d. discrete real
3. epistemic uncertain
a. continuous
b. discrete integer
c. discrete string
d. discrete real
4. state
a. continuous
b. discrete integer
c. discrete string
d. discrete real
Ordering of variable types below this granularity (e.g., from normal
to histogram bin within aleatory uncertain - continuous ) is defined
somewhat arbitrarily, but is enforced consistently throughout the
code.
*Active Variables*
The reason variable types exist is that methods have the capability to
treat variable types differently. All methods have default behavior that
determines which variable types are "active" and will be assigned values
by the method. For example, optimization methods will only vary the
design variables - by default.
The default behavior should be described on each method page, or on
topics pages that relate to classes of methods. In addition, the
default behavior can be modified using the :dakkw:`variables-active`
keyword.
At least one type of variables that are active for the method in use
must have nonzero size (at least 1 active variable) or an input error
message will result.
*Inferred Default Values and Bounds*
The concept of active variables allows any Dakota variable type to be
used in any method context. Some methods, e.g., bound-constrained
optimization or multi-dimensional or centered parameter studies,
require bounds and/or an initial point on the variables, however
uncertain variables may not be naturally defined in terms of these
characteristics.
Distribution lower and upper bounds are explicit portions of the
normal, lognormal, uniform, loguniform, triangular, and beta
specifications, whereas they are implicitly defined for others. For
example, bounds are naturally defined for histogram bin, histogram
point, and interval variables (from the extreme values within the
bin/point/interval specifications) as well as for binomial (0 to
``num_trials``) and hypergeometric (0 to min( ``num_drawn``,
``num_selected``)) variables.
If not specified, distribution bounds are also inferred for normal and
lognormal (if optional bounds are unspecified) as well as for
exponential, gamma, gumbel, frechet, weibull, poisson, negative
binomial, and geometric (which have no bounds specifications); these
bounds are [0, :math:`\mu + 3 \sigma` ] for exponential, gamma, frechet,
weibull, poisson, negative binomial, geometric, and unspecified
lognormal, and [ :math:`\mu - 3 \sigma` , :math:`\mu + 3 \sigma` ] for gumbel
and unspecified normal.
When an intial point is needed and not explcitly specified in user
input, it is assigned as described in the ``initial_point`` or
``initial_state`` specification, e.g.,
:dakkw:`variables-normal_uncertain-initial_point`. For example, uncertain
variables are initialized to their means, where mean values for
bounded normal and bounded lognormal may be further adjusted to
satisfy any user-specified distribution bounds, mean values for
discrete integer range distributions are rounded down to the nearest
integer, and mean values for discrete set distributions are rounded to
the nearest set value.
**Examples**
Several examples follow. In the first example, two continuous design
variables are specified:
.. code-block::
variables,
continuous_design = 2
initial_point 0.9 1.1
upper_bounds 5.8 2.9
lower_bounds 0.5 -2.9
descriptors 'radius' 'location'
In the next example, defaults are employed. In this case,
``initial_point`` will default to a vector of ``0``. values,
``upper_bounds`` will default to vector values of ``DBL_MAX`` (the maximum
number representable in double precision for a particular platform),
``lower_bounds`` will default to a vector of ``-DBL_MAX`` values, and
``descriptors`` will default to a vector of <tt>'cdv_i'</tt> strings,
where ``i`` ranges from one to two:
.. code-block::
variables,
continuous_design = 2
In the following example, the syntax for a normal-lognormal
distribution is shown. One normal and one lognormal uncertain
variable are completely specified by their means and standard
deviations. In addition, the dependence structure between the two
variables is specified using the ``uncertain_correlation_matrix``.
.. code-block::
variables,
normal_uncertain = 1
means = 1.0
std_deviations = 1.0
descriptors = 'TF1n'
lognormal_uncertain = 1
means = 2.0
std_deviations = 0.5
descriptors = 'TF2ln'
uncertain_correlation_matrix = 1.0 0.2
0.2 1.0
An example of the syntax for a state variables specification follows:
.. code-block::
variables,
continuous_state = 1
initial_state 4.0
lower_bounds 0.0
upper_bounds 8.0
descriptors 'CS1'
discrete_state_range = 1
initial_state 104
lower_bounds 100
upper_bounds 110
descriptors 'DS1'
And in a more advanced example, a variables specification containing a
set identifier, continuous and discrete design variables, normal and
uniform uncertain variables, and continuous and discrete state
variables is shown:
.. code-block::
variables,
id_variables = 'V1'
continuous_design = 2
initial_point 0.9 1.1
upper_bounds 5.8 2.9
lower_bounds 0.5 -2.9
descriptors 'radius' 'location'
discrete_design_range = 1
initial_point 2
upper_bounds 1
lower_bounds 3
descriptors 'material'
normal_uncertain = 2
means = 248.89, 593.33
std_deviations = 12.4, 29.7
descriptors = 'TF1n' 'TF2n'
uniform_uncertain = 2
lower_bounds = 199.3, 474.63
upper_bounds = 298.5, 712.
descriptors = 'TF1u' 'TF2u'
continuous_state = 2
initial_state = 1.e-4 1.e-6
descriptors = 'EPSIT1' 'EPSIT2'
discrete_state_set
integer = 1
initial_state = 100
set_values = 100 212 375
descriptors = 'load_case'