  The simplex method for linear programming is known to be highly efficient in
practice, and understanding its performance from a theoretical perspective is
an active research topic. The framework of smoothed analysis, first introduced
by Spielman and Teng (JACM '04) for this purpose, defines the smoothed
complexity of solving a linear program with $d$ variables and $n$ constraints
as the expected running time when Gaussian noise of variance $\sigma^2$ is
added to the LP data. We prove that the smoothed complexity of the simplex
method is $O(\sigma^{-3/2} d^{13/4}\log^{7/4} n)$, improving the dependence on
$1/\sigma$ compared to the previous bound of $O(\sigma^{-2} d^2\sqrt{\log n})$.
We accomplish this through a new analysis of the \emph{shadow bound}, key to
earlier analyses as well. Illustrating the power of our new method, we use our
method to prove a nearly tight upper bound on the smoothed complexity of
two-dimensional polygons.
  We also establish the first non-trivial lower bound on the smoothed
complexity of the simplex method, proving that the \emph{shadow vertex simplex
method} requires at least $\Omega \Big(\min \big(\sigma^{-1/2}
d^{-1/2}\log^{-1/4} d,2^d \big) \Big)$ pivot steps with high probability. A key
part of our analysis is a new variation on the extended formulation for the
regular $2^k$-gon. We end with a numerical experiment that suggests this
analysis could be further improved.

Data Structures and Algorithms