Suppose $g(x_{1:n})$ is the estimate of a gradient, which is calculated at each step of a Stochastic Steepest Descent algorithm. A dataset $x_{1:n}$ is simulated at each step, so if $n$ is small the algorithm is fast but unstable, while if $n$ is large it is slow but stable. So far I have just experimented with many values of n, but maybe somebody knows a better way of determining n.Suppose $Var(g(x_{1:n})) = f(n)$ is known (i.e. I know how the variance of the gradient varies with the sample size), I was considering:a) To minimize a loss function of the type:$$Loss(n)=f(n)+nc.$$b) To plot $f(n)$ against $n \times c$ to try determine a good sample size n visually.In my case $c$ is the time needed to simulate $x_{i}$, for example 0.1sec. I don't think that minimizing a function were different units are summed together makes much sense, so I was wondering whether there is any way to translate CPU times into something more sensible. 
Optimal sample size for Stochastic Steepest Descent
optimization;stochastic