Inertial and relaxed versions of the nonlinear forward-backward method converge weakly to solutions of monotone inclusions, and they specialize to accelerated variants of forward-backward, forward-half-reflect-backward, Chambolle-Pock, and Condat-Vu.
Forward Primal-Dual Half-Forward Algorithm for Splitting Four Operators
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abstract
In this article, we propose a splitting algorithm to find zeros of the sum of four maximally monotone operators in real Hilbert spaces. In particular, we consider a Lipschitzian operator, a cocoercive operator, and a linear composite term. In the case when the Lipschitzian operator is absent, our method reduces to the Condat-V\~u algorithm. On the other hand, when the linear composite term is absent, the algorithm reduces to the Forward-Backward-Half-Forward algorithm (FBHF). Additionally, in each case, the set of step-sizes that guarantee the weak convergence of those methods are recovered. Therefore, our algorithm can be seen as a generalization of Condat-V\~u and FBHF. Moreover, we propose extensions and applications of our method in multivariate monotone inclusions and saddle point problems. Finally, we present a numerical experiment in image deblurring problems.
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Relaxed and Inertial Nonlinear Forward-Backward with Momentum
Inertial and relaxed versions of the nonlinear forward-backward method converge weakly to solutions of monotone inclusions, and they specialize to accelerated variants of forward-backward, forward-half-reflect-backward, Chambolle-Pock, and Condat-Vu.