Optimization methods are at the core of many problems in signal/image processing,
computer vision, and machine learning. For a long time, it has been recognized that looking …

We propose a new first-order splitting algorithm for solving jointly the primal and dual
formulations of large-scale convex minimization problems involving the sum of a smooth …

In this paper, we propose an inertial forward-backward splitting algorithm to compute a zero
of the sum of two monotone operators, with one of the two operators being co-coercive. The …

We consider the problem of solving dual monotone inclusions involving sums of composite
parallel-sum type operators. A feature of this work is to exploit explicitly the properties of the …

We propose an inertial Douglas–Rachford splitting algorithm for finding the set of zeros of
the sum of two maximally monotone operators in Hilbert spaces and investigate its …

This paper introduces a generalized forward-backward splitting algorithm for finding a zero
of a sum of maximal monotone operators B+i=1^nA_i, where B is cocoercive. It involves the …

Recent decades have seen enormous improvements in computational inference for
statistical models; there have been competitive continual enhancements in a wide range of …

This work proposes block-coordinate fixed point algorithms with applications to nonlinear
analysis and optimization in Hilbert spaces. The asymptotic analysis relies on a notion of …

We propose a forward–backward proximal-type algorithm with inertial/memory effects for
minimizing the sum of a nonsmooth function with a smooth one in the nonconvex setting …

Motivated by structures that appear in deep neural networks, we investigate nonlinear
composite models alternating proximity and affine operators defined on different spaces. We …