A Deterministic Analysis for Learning Algorithms with Constant Learning Rates

A novel deterministic approach to the convergence analysis of (stochastic) learning algorithms is presented. The link between the two is a new concept of time-average invariance (TAI) which is a property of deterministic signals but with applications to stochastic signals. It is then extended to the case of perturbed TAI signals.