1Blurb:: 2Truncate the subspace based on eigenvalue energy 3 4Description:: 5Uses a criterion based on the derivative matrix eigenvalue energy. 6 7<b> Usage Tips </b> 8 9This subspace truncation method may work best when working with non-normally 10distributed uncertain variables. If this automated diagnostic does not yield 11desirable results, consider using the explicit \ref model-active_subspace-dimension truncation option or 12one of the other truncation methods. 13 14Topics:: 15 16Examples:: 17Theory:: 18Using the eigenvalue energy truncation metric, the subspace size is determined using the following equation: 19\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] 20where \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. 21 22 23Faq:: 24See_Also:: 25