In this article, we consider the problem of decentralized optimization over time-varying directed networks. The network nodes can access only their local objectives, and aim to collaboratively minimize a global function by exchanging messages with neighbors. Leveraging sparsification, gradient tracking, variance reduction, propose novel communication-efficient scheme that is suitable for resour...

In this article, we study the landscape of an online nonconvex optimization problem, for which input data vary over time and solution is a trajectory rather than single point. To understand complexity finding global introduce notion spurious (i.e., nonglobal) local trajectory as generalization ...

Nowadays, due to inherent complexity of real optimization problems, it has always been a challenging issue to develop a solution algorithm to these problems. Single row facility layout problem (SRFLP) is a NP-hard problem of arranging a number of rectangular facilities with varying length on one side of a straight line with aim of minimizing the weighted sum of the distance between all facility...

In this article, we consider a distributed convex optimization problem over time-varying undirected networks. We propose dual method, primarily averaged network ascent (PANDA), that is proven to converge R-linearly the optimal point given agents' objective functions are strongly and have Lipschitz continuous gradients. Like decomposition, PANDA requires half amount of variable exchanges per ite...

The paper addresses large-scale, convex optimization problems that need to be solved in a distributed way by agents communicating according random time-varying graph. Specifically, the goal of network is minimize sum local costs, while satisfying and coupling constraints. Agents communicate model which edges an underlying connected graph are active at each iteration with certain non-uniform pro...

In this paper, we develop an interior-point method for solving a class of convex optimization problems with timevarying objective and constraint functions. Using log-barrier penalty functions, we propose a continuous-time dynamical system for tracking the (time-varying) optimal solution with an asymptotically vanishing error. This dynamical system is composed of two terms: (i) a correction term...