Webdistributed optimization algorithms including EXTRA. Despite the existence of many distributed convex op-timization algorithms, a substantial number of real-world applications require to address the more challenging non-convex optimization problems, such as dictionary learning [6], power allocation [7], energy efficiency in mobile ad hoc WebThis paper aims to develop distributed algorithms for nonconvex optimization problems with complicated constraints associated with a network. The network can be a physical one, such as an electric power network, where the constraints are nonlinear power flow equations, or an abstract one that represents constraint couplings between decision …

Distributed Zero-Order Algorithms for Nonconvex Multi-Agent ...

Webfor the non-convex loss compared to existing works. We the-oretically analyze the DP-SGD with stagewise learning rate and momentum under the same assumptions used by non … WebAbstract. We study structured convex optimization problems, with additive objective r:= p+ q r := p + q, where r r is ( μ μ -strongly) convex, q q is Lq L q -smooth and convex, and p p is Lp L p -smooth, possibly nonconvex. For such a class of problems, we proposed an inexact accelerated gradient sliding method that can skip the gradient ... tempus spa

18-667: Algorithms for Large-scale Distributed Machine Learning …

WebDistributed multi-agent optimization finds many applications in distributed learning, control, estimation, etc. Most existing algorithms assume knowledge of first-order information of the objective and have been analyzed for convex problems. However, there are situations where the objective is nonconvex, and one can only evaluate the function ... http://kkpatel.ttic.edu/ WebAbstract. This paper is about distributed derivative-based algorithms for solving optimization problems with a separable (potentially nonconvex) objective function and … tempus srl