SGD for Cox proportional hazards model

I am wondering if there's any implementation or theory about using stochastic gradient descent for estimation parameters in Cox proportional hazards model. Normally in that model the partial-log-likelihood (which for right censoring that is independent and uninformative about parameters is a true log-likelihood)

$$\ell_p(\beta) = \sum\limits_{i=1}^{K}X_i'\beta - \sum\limits_{i=1}^{K}\log\Big(\sum\limits_{l\in \mathscr{R}(t_i)}^{}e^{X_l'\beta}\Big)$$

where

• $K$ denotes number of failure times,
• $\mathscr{R}(t_i)$ is a risk set at time $t_i$

is estimated using the first and second derivatives and Newton-Raphson algorithm.

I was wondering that if the first derivative can be formulated as a sum of elements in which every element corresponds only to information from 1 observation, then the estimation with the stochastic gradient descent would be easy, but I can't transform this derivative to such form:

$$U_k(\beta)=\dfrac{\partial\ell_k(\beta)}{\partial\beta_k}=\sum\limits_{i=1}^{K}\Big(X_{ik}-A_{ik}\Big), A_{ik} = \dfrac{\sum X_{lk} e^{X_l'\beta}}{\sum e^{X_l'\beta}}$$ and the sums in $A_{ik}$ are over risk set.

Does anyone heard about any articles applying SGD to Cox proportional hazards model? It might have great applications in large-scale cox models.