Blurb::
Truncate the subspace based on eigenvalue energy

Description::
Uses a criterion based on the derivative matrix eigenvalue energy.

<b> Usage Tips </b>

This subspace truncation method may work best when working with non-normally
distributed uncertain variables. If this automated diagnostic does not yield
desirable results, consider using the explicit \ref model-active_subspace-dimension truncation option or
one of the other truncation methods.

Topics::

Examples::
Theory::
Using the eigenvalue energy truncation metric, the subspace size is determined using the following equation:
\f[n = \inf \left\lbrace d \in \mathbf{Z} \quad\middle|\quad 1 \le d \le N \quad \wedge\quad 1 - \frac{\sum_{i = 1}^{d} \lambda_i}{\sum_{i = 1}^{N} \lambda_i} \,<\, \epsilon \right\rbrace \f]
where \f$\epsilon\f$ is the \ref model-active_subspace-truncation_method-energy-truncation_tolerance, \f$n\f$ is the estimated subspace size, \f$N\f$ is the size of the full space, and \f$\lambda_i\f$ are the eigenvalues of the derivative matrix.


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