I am working on an optimisation algorithm using the results from this paper. On page 16 it says "For Riemannian SGD, we report the result of using Euclidean retraction", but up to that point in the paper they used exponential maps instead and never clarify what exactly is an Euclidean retraction. I could not find anything online and this is not my area so maybe I'm missing something basic.
Edit: They are using this Euclidean retraction as a substitute for the exponential map, which they define as $Exp_{\Sigma}(\xi) = \Sigma *\text{exp}(\Sigma^{-1} \xi)$ (for a symmetric matrix $\xi$ in the tangent space of $\Sigma$, which is an element of the manifold of symmetric positive definite matrices $\mathbb{M}$), and they use this to project a gradient descent step back into the mainifold, so I'm trying to find the formula they were referring to by "Euclidean retraction", which hopefully is less computationally expensive.
Here are the classical definitions:
Remark. Some authors required that for all $t\in [0,1]$, $H(\cdot,t)_{\vert A}\colon A\rightarrow A$, the others called such a data a strong deformation retract.
Remark. Some authors will only require that $j(A)$ is a retraction of $U$, they ask only for the existence of the map $r$ in definition $1$, not a deformation retract.
Informally, a Euclidean retract is homotopical to a Euclidean space.
Does it help?