Тип публикации: статья из журнала
Год издания: 2024
Идентификатор DOI: 10.3390/math12172796
Аннотация: <jats:p>We propose a novel distributed method for non-convex optimization problems with coupling equality and inequality constraints. This method transforms the optimization problem into a specific form to allow distributed implementation of modified gradient descent and Newton's methods so that they operate as if they were distribПоказать полностьюuted. We demonstrate that for the proposed distributed method: (i) communications are significantly less time-consuming than oracle calls, (ii) its convergence rate is equivalent to the convergence of Newton's method concerning oracle calls, and (iii) for the cases when oracle calls are more expensive than communication between agents, the transition from a centralized to a distributed paradigm does not significantly affect computational time. The proposed method is applicable when the objective function is twice differentiable and constraints are differentiable, which holds for a wide range of machine learning methods and optimization setups.</jats:p>
Журнал: Mathematics
Выпуск журнала: Т. 12, № 17
Номера страниц: 2796
ISSN журнала: 22277390
Место издания: Basel
Издатель: MDPI