A proximal point algorithm revisited and extended

Abstract

This Note is inspired by the recent paper by Djafary Rouhani and Moradi [J. Optim. Theory Appl. 172 (2017) 222-235], where a proximal point algorithm proposed by Boikanyo and Moroşanu [Optim. Lett. 7 (2013) 415-420] is discussed. We start with a brief history of the subject and then propose and analyse the following more general algorithm for approximating the zeroes of a maximal monotone operator $A$ in real Hilbert space $H$ $$x_{n+1}=(I+{\beta }_{n}A{)}^{-1}(u_n+{\alpha }_n(x_n+e_n)),n\ge 0,$$ where $x_0\in H$ is a given starting point, $u_n\to u$ is a given sequence in $H$, $R\ni {\alpha }_n\to 0$, and $(e_n)$ is the error sequence satisfying ${\alpha }_n{e}_n\to 0$. Besides the main result on the strong convergence of $(x_n)$, we discuss some particular cases, including the approximation of minimizers of convex functionals, explain how to use our algorithm in practice, and present some simulations to illustrate the applicability of our algorithm.

arXiv comment10 pages