Stochastic gradient descent (SGD) and projected stochastic (PSGD) are scalable algorithms to compute model parameters in unconstrained constrained optimization problems. In comparison with SGD, PSGD forces its iterative values into the parameter space via projection. From a statistical point of view, this paper studies limiting distribution PSGD-based estimate when true satisfy some linear-equa...