  It has been recognized for quite some time that for some matrices the spectra
are not enough to tell the complete story of the dynamics of the system, even
for linear ODEs. While it is true that the eigenvalues control the asymptotic
behavior of the system, if the matrix representing the system is non-normal,
short term transients may appear in the linear system. Recently it has been
recognized that since the matrices representing directed networks are
non-normal, analysis based on spectra alone may be misleading. Both a normal
and a non-normal system may be stable according to the master stability
paradigm, but the non-normal system may have an arbitrarily small attraction
basin to the synchronous state whereas an equivalent normal system may have a
significantly larger sync basin. This points to the need to study
synchronization in non-normal networks more closely. In this work, various
tools will be utilized to examine synchronization in directed networks,
including pseudospectra, an adaption of pseudospectra that we will call
Laplacian pseudospectra. We define a resulting concept that we call Laplacian
pseudospectral resilience (LPR). It will be shown that LPR outperforms other
scalar measures for estimating the stability of the synchronous state to finite
perturbations in a class of networks known as optimal networks. Finally we find
that the ideal choice of optimal network, with an eye toward synchronization,
is the one which minimizes LPR
