A First-Order Splitting Method for Solving a Large-Scale Composite Convex Optimization Problem

Abstract

The forward-backward operator splitting algorithm is one of the most important methods for solving the optimization problem of the sum of two convex functions, where one is differentiable with a Lipschitz continuous gradient and the other is possibly nonsmooth but proximable. It is convenient to solve some optimization problems in the form of dual or primal-dual problems. Both methods are mature in theory. In this paper, we construct several efficient first-order splitting algorithms for solving a multi-block composite convex optimization problem. The objective function includes a smooth function with a Lipschitz continuous gradient, a proximable convex function that may be nonsmooth, and a finite sum of a composition of a proximable function and a bounded linear operator. To solve such an optimization problem, we transform it into the sum of three convex functions by defining an appropriate inner product space. On the basis of the dual forward-backward splitting algorithm and the primal-dual forward-backward splitting algorithm, we develop several iterative algorithms that involve only computing the gradient of the differentiable function and proximity operators of related convex functions. These iterative algorithms are matrix-inversion-free and completely splitting algorithms. Finally, we employ the proposed iterative algorithms to solve a regularized general prior image constrained compressed sensing (PICCS) model that is derived from computed tomography (CT) image reconstruction under sparse sampling of projection measurements. Numerical results show that the proposed iterative algorithms outperform other algorithms.

An inertial three-operator splitting algorithm with applications to image inpainting

“…In recent years, the monotone inclusion problems with the sum of more than two operators have been received much attention. See, for example [4][5][6][7][8].…”

An inertial three-operator splitting algorithm with applications to image inpainting

“…The traditional operator splitting algorithms include the forward-backward splitting algorithm [31], the Douglas-Rachford splitting algorithm [27], and the forward-backward-forward splitting algorithm [44], which are originally designed for solving the monotone inclusion of the sum of two maximally monotone operators, where one of which is assumed to be cocoercive or just Lipschitz continuous. In recent years, the monotone inclusion problems with the sum of more than two operators have been received much attention; see, for example, [15,16,17,20,41,45,46,48] and the references therein.…”

Convergence analysis of an inexact inertial Krasnoselskii-Mann algorithm with applications

“…See for example [1][2][3][4][5][6][7] and references therein. Many efficient iterative algorithms for solving composite convex optimization problems include the primal-dual fixed point proximity algorithm [8,9], the Davis-Yin's three-operator splitting algorithm [10,11] and the primal-dual hybrid gradient algorithm and its variants [12,13] that can be formulated as a fixed point problem of nonexpansive operators.…”