  We study a class of optimization problems including matrix scaling, matrix
balancing, multidimensional array scaling, operator scaling, and tensor scaling
that arise frequently in theory and in practice. Some of these problems, such
as matrix and array scaling, are convex in the Euclidean sense, but others such
as operator scaling and tensor scaling are \emph{geodesically convex} on a
different Riemannian manifold. Trust region methods, which include
box-constrained Newton's method, are known to produce high precision solutions
very quickly for matrix scaling and matrix balancing (Cohen et. al., FOCS 2017,
Allen-Zhu et. al. FOCS 2017), and result in polynomial time algorithms for some
geodesically convex problems like operator scaling (Garg et. al. STOC 2018,
B\"urgisser et. al. FOCS 2019). One is led to ask whether these guarantees also
hold for multidimensional array scaling and tensor scaling.
  We show that this is not the case by exhibiting instances with exponential
\emph{diameter bound}: we construct polynomial-size instances of 3-dimensional
array scaling and 3-tensor scaling whose approximate solutions all have doubly
exponential condition number. Moreover, we study convex-geometric notions of
complexity known as margin and gap, which are used to bound the running times
of all existing optimization algorithms for such problems. We show that margin
and gap are exponentially small for several problems including array scaling,
tensor scaling and polynomial scaling. Our results suggest that it is
impossible to prove polynomial running time bounds for tensor scaling based on
diameter bounds alone. Therefore, our work motivates the search for analogues
of more sophisticated algorithms, such as interior point methods, for
geodesically convex optimization that do not rely on polynomial diameter
bounds.
