  We discuss an approach to signal recovery in Generalized Linear Models (GLM)
in which the signal estimation problem is reduced to the problem of solving a
stochastic monotone variational inequality (VI). The solution to the stochastic
VI can be found in a computationally efficient way, and in the case when the VI
is strongly monotone we derive finite-time upper bounds on the expected
$\|\cdot\|_2^2$ error converging to 0 at the rate $O(1/K)$ as the number $K$ of
observations grows. Our structural assumptions are essentially weaker than
those necessary to ensure convexity of the optimization problem resulting from
Maximum Likelihood estimation. In hindsight, the approach we promote can be
traced back directly to the ideas behind the Rosenblatt's perceptron algorithm.
