Suvendu Ranjan Pattanaik Shi et al. \cite{shi2022understanding} propose acceleration methods to solve smooth convex optimization problems. In our work, we focus on the general unconstrained composite non-smooth convex optimization problem. We provide an inertial forward-backward algorithm with subgradient correction, derived through time discretization of the ODE, as studied by Shi et al. We achieve the rate of convergence … Read more
In this paper, we consider an unconstrained composite convex optimisation problem. We propose an inertial forward–backward algorithm derived from an implicit– explicit discretisation of a second-order dynamical system with Hessian-driven damping. For α ≥ 3, we establish an O(1/d^2) convergence rate for the objective value gap. Furthermore, when α > 3, we prove that the … Read more
In this work, we investigate the accelerated proximal gradient algorithm (APGα) for weakly convex composite optimization problems. Building upon the framework of B¨ohm and Wright, and additionally assuming that f is convex and coercive while g is bounded below, we establish an objective residual convergence rate of O(1⁄j²) for α≥3. Moreover, when α›3, this rate … Read more
The Douglas-Rachford algorithm is a classical and a successful method for solving the feasibility problems. Here, we provide a region for global convergence of the algorithm for the feasibility problem involving a disc and a circle in the Euclidean space of dimension two. Citation1. Borwein, J.M., Sims, B.: The Douglas-Rachford algorithm in the absence of … Read more
In this paper, we show that for a large class of optimization problems, the Lagrange multiplier rule can be derived from the so-called approximate multiplier rule. In establishing the link between the approximate and the exact multiplier rule we first derive an approximate multiplier rule for a very general class of optimization problems using the … Read more