Recently, alternating direction method of multipliers (ADMM) attracts much attentions from
various fields and there are many variant versions tailored for different models. Moreover, its …

Over the past few years, significant advancements have been made in the field of machine
learning (ML) to address resource management, interference management, autonomy, and …

Since its beginning, optimization has played a vital role in data science. The analysis and
solution methods for many statistical and machine learning models rely on optimization. The …

The dawn of quantum computing brings on a revolution in the way combinatorially complex
power system problems such as Unit Commitment are solved. The Unit Commitment …

Linearized alternating direction method of multipliers (ADMM) as an extension of ADMM has
been widely used to solve linearly constrained problems in signal processing, machine …

This paper proposes a two-level distributed algorithmic framework for solving the AC optimal
power flow (OPF) problem with convergence guarantees. The presence of highly nonconvex …

Consider the minimization of a nonconvex differentiable function over a bounded
polyhedron. A popular primal-dual first-order method for this problem is to perform a gradient …

Solving combinatorial optimization problems on current noisy quantum devices is currently
being advocated for (and restricted to) binary polynomial optimization with equality …

In this paper, an inexact proximal-point penalty method is studied for constrained
optimization problems, where the objective function is non-convex, and the constraint …

In this paper, we consider a proximal linearized alternating direction method of multipliers, or
PL-ADMM, for solving linearly constrained nonconvex and possibly nonsmooth optimization …