Projected stochastic gradient descent
= 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 $x_{s+1}=\Pi_C(x_s-\eta_sG_s)$.