  Frank-Wolfe methods are popular for optimization over a polytope. One of the
reasons is because they do not need projection onto the polytope but only
linear optimization over it. To understand its complexity, Lacoste-Julien and
Jaggi introduced a condition number for polytopes and showed linear convergence
for several variations of the method. The actual running time can still be
exponential in the worst case (when the condition number is exponential). We
study the smoothed complexity of the condition number, namely the condition
number of small random perturbations of the input polytope and show that it is
polynomial for any simplex and exponential for general polytopes. Our results
also apply to other condition measures of polytopes that have been proposed for
the analysis of Frank-Wolfe methods: vertex-facet distance (Beck and Shtern)
and facial distance (Pe\~na and Rodr\'iguez).
  Our argument for polytopes is a refinement of an argument that we develop to
study the conditioning of random matrices. The basic argument shows that for
$c>1$ a $d$-by-$n$ random Gaussian matrix with $n \geq cd$ has a $d$-by-$d$
submatrix with minimum singular value that is exponentially small with high
probability. This has consequences on results about the robust uniqueness of
tensor decompositions.
