873 citations · 1.2k across the 12 of their papers we have counts for
15 papers
Geometric Convergence of Gradient Play Algorithms for Distributed Nash Equilibrium Seeking
Tatiana Tatarenko, Wei Shi, Angelia Nedich
We study distributed algorithms for seeking a Nash equilibrium in a class of non-cooperative convex games with strongly monotone mappings. Each player has access to her own smooth…
Push-Pull Gradient Methods for Distributed Optimization in Networks
Shi Pu, Wei Shi, Jinming Xu +1
In this paper, we focus on solving a distributed convex optimization problem in a network, where each agent has its own convex cost function and the goal is to minimize the sum of…
Accelerating Incremental Gradient Optimization with Curvature Information
Hoi-To Wai, Wei Shi, Cesar A. Uribe +2
This paper studies an acceleration technique for incremental aggregated gradient ({\sf IAG}) method through the use of \emph{curvature} information for solving strongly convex fini…
A Push-Pull Gradient Method for Distributed Optimization in Networks
Shi Pu, Wei Shi, Jinming Xu +1
In this paper, we focus on solving a distributed convex optimization problem in a network, where each agent has its own convex cost function and the goal is to minimize the sum of…
Curvature-aided Incremental Aggregated Gradient Method
Hoi-To Wai, Wei Shi, Angelia Nedic +1
We propose a new algorithm for finite sum optimization which we call the curvature-aided incremental aggregated gradient (CIAG) method. Motivated by the problem of training a class…
Distributed Nash Equilibrium Seeking under Partial-Decision Information via the Alternating Direction Method of Multipliers
Farzad Salehisadaghiani, Wei Shi, Lacra Pavel
In this paper, we consider the problem of finding a Nash equilibrium in a multi-player game over generally connected networks. This model differs from a conventional setting in tha…
Improved Convergence Rates for Distributed Resource Allocation
Angelia Nedić, Alex Olshevsky, Wei Shi
In this paper, we develop a class of decentralized algorithms for solving a convex resource allocation problem in a network of agents, where the agent objectives are decoupled…
Geometrically Convergent Distributed Optimization with Uncoordinated Step-Sizes
Angelia Nedić, Alex Olshevsky, Wei Shi +1
A recent algorithmic family for distributed optimization, DIGing's, have been shown to have geometric convergence over time-varying undirected/directed graphs. Nevertheless, an ide…
Achieving Geometric Convergence for Distributed Optimization over Time-Varying Graphs
Angelia Nedich, Alex Olshevsky, Wei Shi
This paper considers the problem of distributed optimization over time-varying graphs. For the case of undirected graphs, we introduce a distributed algorithm, referred to as DIGin…
A Decentralized Second-Order Method for Dynamic Optimization
Aryan Mokhtari, Wei Shi, Qing Ling +1
This paper considers decentralized dynamic optimization problems where nodes of a network try to minimize a sequence of time-varying objective functions in a real-time scheme. At e…
A Decentralized Second-Order Method with Exact Linear Convergence Rate for Consensus Optimization
Aryan Mokhtari, Wei Shi, Qing Ling +1
This paper considers decentralized consensus optimization problems where different summands of a global objective function are available at nodes of a network that can communicate…
Decentralized Quadratically Approximated Alternating Direction Method of Multipliers
Aryan Mokhtari, Wei Shi, Qing Ling +1
This paper considers an optimization problem that components of the objective function are available at different nodes of a network and nodes are allowed to only exchange informat…
DQM: Decentralized Quadratically Approximated Alternating Direction Method of Multipliers
Aryan Mokhtari, Wei Shi, Qing Ling +1
This paper considers decentralized consensus optimization problems where nodes of a network have access to different summands of a global objective function. Nodes cooperate to min…
EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization
Wei Shi, Qing Ling, Gang Wu +1
Recently, there have been growing interests in solving consensus optimization problems in a multi-agent network. In this paper, we develop a decentralized algorithm for the consens…
On the Linear Convergence of the ADMM in Decentralized Consensus Optimization
Wei Shi, Qing Ling, Kun Yuan +2
In decentralized consensus optimization, a connected network of agents collaboratively minimize the sum of their local objective functions over a common decision variable, where th…