.. _model-surrogate-local-taylor_series:

"""""""""""""
taylor_series
"""""""""""""


Construct a Taylor Series expansion around a point


.. toctree::
   :hidden:
   :maxdepth: 1



**Specification**

- *Alias:* None

- *Arguments:* None


**Description**


The Taylor series model is purely a local approximation method. That
is, it provides local trends in the vicinity of a single point in
parameter space.

The order of the
Taylor series may be either first-order or second-order, which is
automatically determined from the gradient and Hessian specifications
in the responses specification (see :dakkw:`responses` for info on
how to specify gradients and Hessians)
for the truth model

*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.*



**Theory**


The first-order Taylor series expansion is:
\anchor eq-taylor1
\f{equation}
\hat{f}({\bf x}) \approx f({\bf x}_0) + \nabla_{\bf x} f({\bf x}_0)^T
({\bf x} - {\bf x}_0)
\f}
and the second-order expansion is:
\anchor eq-taylor2
\f{equation}
\hat{f}({\bf x}) \approx f({\bf x}_0) + \nabla_{\bf x} f({\bf x}_0)^T
({\bf x} - {\bf x}_0) + \frac{1}{2} ({\bf x} - {\bf x}_0)^T
\nabla^2_{\bf x} f({\bf x}_0) ({\bf x} - {\bf x}_0)
\f}

where :math:`{\bf x}_0`  is the expansion point in :math:`n` -dimensional parameter
space and :math:`f({\bf x}_0)` , :math:`\nabla_{\bf x} f({\bf x}_0)` , and
:math:`\nabla^2_{\bf x} f({\bf x}_0)`  are the computed response value,
gradient, and Hessian at the expansion point, respectively.

As
dictated by the responses specification used in building the local
surrogate, the gradient may be analytic or numerical and the Hessian
may be analytic, numerical, or based on quasi-Newton secant updates.

In general, the Taylor series model is accurate only in the region of
parameter space that is close to :math:`{\bf x}_0`  . While the accuracy is
limited, the first-order Taylor series model reproduces the correct
value and gradient at the point :math:`\mathbf{x}_{0}` , and the second-order
Taylor series model reproduces the correct value, gradient, and
Hessian. This consistency is useful in provably-convergent
surrogate-based optimization. The other surface fitting methods do not
use gradient information directly in their models, and these methods
rely on an external correction procedure in order to satisfy the
consistency requirements of provably-convergent SBO.


