On the convergence of an inertial proximal algorithm with a Tikhonov regularization term

This paper deals with an inertial proximal algorithm that contains a Tikhonov regularization term, in connection to the minimization problem of a convex lower semicontinuous function $f$. We show that for appropriate Tikhonov regularization parameters the value of the objective function in the sequences generated by our algorithm converge fast (with arbitrary rate) to the global minimum of the objective function and the generated sequences converge weakly to a minimizer of the objective function. We also obtain the fast convergence of subgradients and the discrete velocities towards zero and some sum estimates. Further, we obtain strong convergence results for the generated sequences and also fast convergence for the function values and discrete velocities for the same constellation of the parameters involved. Our analysis reveals that the extrapolation coefficient, the stepsize and the Tikhonov regularization coefficient are strongly correlated and there is a critical setting of the parameters that separates the cases when strong convergence results or weak convergence results can be obtained.