33 citations · 113 across the 20 of their papers we have counts for
20 papers
Distributed Random Reshuffling Methods with Improved Convergence
Kun Huang, Linli Zhou, Shi Pu
This paper proposes two distributed random reshuffling methods, namely Gradient Tracking with Random Reshuffling (GT-RR) and Exact Diffusion with Random Reshuffling (ED-RR), to sol…
A Linearly Convergent Robust Compressed Push-Pull Method for Decentralized Optimization
Yiwei Liao, Zhuorui Li, Shi Pu
In the modern paradigm of multi-agent networks, communication has become one of the main bottlenecks for decentralized optimization, where a large number of agents are involved in…
Private and Accurate Decentralized Optimization via Encrypted and Structured Functional Perturbation
Yijie Zhou, Shi Pu
We propose a decentralized optimization algorithm that preserves the privacy of agents' cost functions without sacrificing accuracy, termed EFPSN. The algorithm adopts Paillier cry…
A Compressed Gradient Tracking Method for Decentralized Optimization with Linear Convergence
Yiwei Liao, Zhuorui Li, Kun Huang +1
Communication compression techniques are of growing interests for solving the decentralized optimization problem under limited communication, where the global objective is to minim…
Distributed Random Reshuffling over Networks
Kun Huang, Xiao Li, Andre Milzarek +2
In this paper, we consider distributed optimization problems where agents, each possessing a local cost function, collaboratively minimize the average of the local cost functio…
Optimal Gradient Tracking for Decentralized Optimization
Zhuoqing Song, Lei Shi, Shi Pu +1
In this paper, we focus on solving the decentralized optimization problem of minimizing the sum of objective functions over a multi-agent network. The agents are embedded in an…
Provably Accelerated Decentralized Gradient Method Over Unbalanced Directed Graphs
Zhuoqing Song, Lei Shi, Shi Pu +1
We consider the decentralized optimization problem, where a network of agents aims to collaboratively minimize the average of their individual smooth and convex objective funct…
Compressed Gradient Tracking for Decentralized Optimization Over General Directed Networks
Zhuoqing Song, Lei Shi, Shi Pu +1
In this paper, we propose two communication efficient decentralized optimization algorithms over a general directed multi-agent network. The first algorithm, termed Compressed Push…
Compressed Gradient Tracking Methods for Decentralized Optimization with Linear Convergence
Yiwei Liao, Zhuorui Li, Kun Huang +1
Communication compression techniques are of growing interests for solving the decentralized optimization problem under limited communication, where the global objective is to minim…
A general framework for decentralized optimization with first-order methods
Ran Xin, Shi Pu, Angelia Nedić +1
Decentralized optimization to minimize a finite sum of functions over a network of nodes has been a significant focus within control and signal processing research due to its natur…
A Robust Gradient Tracking Method for Distributed Optimization over Directed Networks
Shi Pu
In this paper, we consider the problem of distributed consensus optimization over multi-agent networks with directed network topology. Assuming each agent has a local cost function…
Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning
Shi Pu, Alex Olshevsky, Ioannis Ch. Paschalidis
We provide a discussion of several recent results which, in certain scenarios, are able to overcome a barrier in distributed stochastic optimization for machine learning. Our focus…
A Sharp Estimate on the Transient Time of Distributed Stochastic Gradient Descent
Shi Pu, Alex Olshevsky, Ioannis Ch. Paschalidis
This paper is concerned with minimizing the average of cost functions over a network in which agents may communicate and exchange information with each other. We consider the s…
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…
Swarming for Faster Convergence in Stochastic Optimization
Shi Pu, Alfredo Garcia
We study a distributed framework for stochastic optimization which is inspired by models of collective motion found in nature (e.g., swarming) with mild communication requirements.…
Distributed Stochastic Gradient Tracking Methods
Shi Pu, Angelia Nedić
In this paper, we study the problem of distributed multi-agent optimization over a network, where each agent possesses a local cost function that is smooth and strongly convex. The…
A Distributed Stochastic Gradient Tracking Method
Shi Pu, Angelia Nedić
In this paper, we study the problem of distributed multi-agent optimization over a network, where each agent possesses a local cost function that is smooth and strongly convex. The…
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…
A Flocking-based Approach for Distributed Stochastic Optimization
Shi Pu, Alfredo Garcia
In recent years, the paradigm of cloud computing has emerged as an architecture for computing that makes use of distributed (networked) computing resources. In this paper, we consi…
Iterative Mechanisms for Electricity Markets
Shi Pu, Alfredo Garcia
In order to deal with market power that sporadically results from contingencies (e.g., severe weather, plant outages) most electricity markets have institutions in charge of monito…