 Projected stochastic gradient descent

Projected stochastic gradient descent takes a stochastic-gradient step and then projects the result onto a closed feasible set. For a convex set $C$ it has the form $x_{s+1}={\Pi }_C(x_s-{\eta }_s{G}_s)$.