  We investigate the effect of an $\varepsilon$-room of perturbation tolerance
on symmetric tensor decomposition. To be more precise, suppose a real symmetric
$d$-tensor $f$, a norm $||.||$ on the space of symmetric $d$-tensors, and
$\varepsilon >0$ are given. What is the smallest symmetric tensor rank in the
$\varepsilon$-neighborhood of $f$? In other words, what is the symmetric tensor
rank of $f$ after a clever $\varepsilon$-perturbation? We prove two theorems
and develop three corresponding algorithms that give constructive upper bounds
for this question. With expository goals in mind; we present probabilistic and
convex geometric ideas behind our results, reproduce some known results, and
point out open problems.
