  Computing spectra is a central problem in computational mathematics with an
abundance of applications throughout the sciences. However, in many
applications gaining an approximation of the spectrum is not enough. Often it
is vital to determine geometric features of spectra such as Lebesgue measure,
capacity or fractal dimensions, different types of spectral radii and numerical
ranges, or to detect essential spectral gaps and the corresponding failure of
the finite section method. Despite new results on computing spectra and the
substantial interest in these geometric problems, there remain no general
methods able to compute such geometric features of spectra of
infinite-dimensional operators. We provide the first algorithms for the
computation of many of these longstanding problems (including the above). As
demonstrated with computational examples, the new algorithms yield a library of
new methods. Recent progress in computational spectral problems in infinite
dimensions has led to the Solvability Complexity Index (SCI) hierarchy, which
classifies the difficulty of computational problems. These results reveal that
infinite-dimensional spectral problems yield an intricate infinite
classification theory determining which spectral problems can be solved and
with which type of algorithm. This is very much related to S. Smale's
comprehensive program on the foundations of computational mathematics initiated
in the 1980s. We classify the computation of geometric features of spectra in
the SCI hierarchy, allowing us to precisely determine the boundaries of what
computers can achieve (in any model of computation) and prove that our
algorithms are optimal. We also provide a new universal technique for
establishing lower bounds in the SCI hierarchy, which both greatly simplifies
previous SCI arguments and allows new, formerly unattainable, classifications.
