Prediction-correction splittings for time-varying optimization with intermittent observations

We study the solution of a time-varying optimization problem which is observed, i.e., it is known, only intermittently. We propose three approaches based on the prediction-correction scheme for solving this problem by exploiting splitting methods. We present convergence results in mean to a bounded asymptotical error, and showcase them in a numerical example featuring a regression problem.