  The utilization of online stochastic algorithms is popular in large-scale
learning settings due to their ability to compute updates on the fly, without
the need to store and process data in large batches. When a constant step-size
is used, these algorithms also have the ability to adapt to drifts in problem
parameters, such as data or model properties, and track the optimal solution
with reasonable accuracy. Building on analogies with the study of adaptive
filters, we establish a link between steady-state performance derived under
stationarity assumptions and the tracking performance of online learners under
random walk models. The link allows us to infer the tracking performance from
steady-state expressions directly and almost by inspection.
