no code implementations • 27 Oct 2021 • Vivek Borkar, Shuhang Chen, Adithya Devraj, Ioannis Kontoyiannis, Sean Meyn
The paper concerns the $d$-dimensional stochastic approximation recursion, $$ \theta_{n+1}= \theta_n + \alpha_{n + 1} f(\theta_n, \Phi_{n+1}) $$ where $ \{ \Phi_n \}$ is a stochastic process on a general state space, satisfying a conditional Markov property that allows for parameter-dependent noise.
no code implementations • 30 Sep 2020 • Shuhang Chen, Adithya Devraj, Andrey Bernstein, Sean Meyn
(ii) With gain $a_t = g/(1+t)$ the results are not as sharp: the rate of convergence $1/t$ holds only if $I + g A^*$ is Hurwitz.