We present a new variant of the Chambolle–Pock primal–dual algorithm with Bregman
distances, analyze its convergence, and apply it to the centering problem in sparse …

In this paper we generalize the Interior Point-Proximal Method of Multipliers (IP-PMM)
presented in Pougkakiotis and Gondzio (Comput Optim Appl 78: 307–351, 2021. https://doi …

This paper concentrates on a class of decentralized convex optimization problems subject to
local feasible sets, equality and inequality constraints, where the global objective function …

A new relaxed variant of interior point method for low-rank semidefinite programming
problems is proposed in this paper. The method is a step outside of the usual interior point …

Availability of reliable and extended datasets of recorded power output from renewables is
nowadays seen as one of the key drivers to improve the design and control of smart energy …

A regularized version of the primal-dual Interior Point Method (IPM) for the solution of
Semidefinite Programming Problems (SDPs) is presented in this paper. Leveraging on the …

The primal-dual interior point method (IPM) is widely regarded as the most efficient IPM
variant for linear optimization. In this paper, we demonstrate that the improved stability of the …

Quantum computing has the potential to speed up some optimization methods. One can use
quantum computers to solve linear systems via Quantum Linear System Algorithms (QLSAs) …

We consider the sequence acceleration problem for the alternating direction method of
multipliers (ADMM) applied to a class of equality-constrained problems with strongly convex …

Proximal methods are an important class of algorithms for solving nonsmooth, constrained,
large-scale or distributed optimization problems. Because of their flexibility and scalability …