3 citations · 9 across the 17 of their papers we have counts for
17 papers
Anderson Acceleration for Distributed Constrained Optimization over Time-varying Networks
Haijuan Liu, Xuyang Wu
This paper applies the Anderson Acceleration (AA) technique to accelerate the Fenchel dual gradient method (FDGM) to solve constrained optimization problems over time-varying netwo…
Historical Information Accelerates Decentralized Optimization: A Proximal Bundle Method
Zhao Zhu, Yu-Ping Tian, Xuyang Wu
Historical information, such as past function values or gradients, has significant potential to enhance decentralized optimization methods for two key reasons: first, it provides r…
A Unified Dual Consensus Approach to Distributed Optimization with Globally-Coupled Constraints
Zixuan Liu, Xuyang Wu, Dandan Wang +1
This article explores distributed convex optimization with globally-coupled constraints, where the objective function is a general nonsmooth convex function, the constraints includ…
Globally-Constrained Decentralized Optimization with Variable Coupling
Dandan Wang, Xuyang Wu, Zichong Ou +1
Many realistic decision-making problems in networked scenarios, such as formation control and collaborative task offloading, often involve complicatedly entangled local decisions,…
Achieving violation-free distributed optimization under coupling constraints
Changxin Liu, Xiao Tan, Xuyang Wu +2
Constraint satisfaction is a critical component in a wide range of engineering applications, including but not limited to safe multi-agent control and economic dispatch in power sy…
Asynchronous Distributed Optimization with Delay-free Parameters
Xuyang Wu, Changxin Liu, Sindri Magnusson +1
Existing asynchronous distributed optimization algorithms often use diminishing step-sizes that cause slow practical convergence, or use fixed step-sizes that depend on and decreas…
Delay-agnostic Asynchronous Coordinate Update Algorithm
Xuyang Wu, Changxin Liu, Sindri Magnusson +1
We propose a delay-agnostic asynchronous coordinate update algorithm (DEGAS) for computing operator fixed points, with applications to asynchronous optimization. DEGAS includes nov…
Delay-agnostic Asynchronous Distributed Optimization
Xuyang Wu, Changxin Liu, Sindri Magnusson +1
Existing asynchronous distributed optimization algorithms often use diminishing step-sizes that cause slow practical convergence, or fixed step-sizes that depend on an assumed uppe…
Rate analysis of dual averaging for nonconvex distributed optimization
Changxin Liu, Xuyang Wu, Xinlei Yi +2
This work studies nonconvex distributed constrained optimization over stochastic communication networks. We revisit the distributed dual averaging algorithm, which is known to conv…
Distributed Safe Resource Allocation using Barrier Functions
Xuyang Wu, Sindri Magnusson, Mikael Johansson
Resource allocation plays a central role in many networked systems such as smart grids, communication networks and urban transportation systems. In these systems, many constraints…
Optimal convergence rates of totally asynchronous optimization
Xuyang Wu, Sindri Magnusson, Hamid Reza Feyzmahdavian +1
Asynchronous optimization algorithms are at the core of modern machine learning and resource allocation systems. However, most convergence results consider bounded information dela…
Delay-adaptive step-sizes for asynchronous learning
Xuyang Wu, Sindri Magnusson, Hamid Reza Feyzmahdavian +1
In scalable machine learning systems, model training is often parallelized over multiple nodes that run without tight synchronization. Most analysis results for the related asynchr…
A New Family of Feasible Methods for Distributed Resource Allocation
Xuyang Wu, Sindri Magnusson, Mikael Johansson
Distributed resource allocation is a central task in network systems such as smart grids, water distribution networks, and urban transportation systems. When solving such problems…
Distributed Optimization with Coupling Constraints
Xuyang Wu, He Wang, Jie Lu
In this paper, we develop a novel distributed algorithm for addressing convex optimization with both nonlinear inequality and linear equality constraints, where the objective funct…
A Unifying Approximate Method of Multipliers for Distributed Composite Optimization
Xuyang Wu, Jie Lu
This paper investigates solving convex composite optimization on an undirected network, where each node, privately endowed with a smooth component function and a nonsmooth one, is…
Decentralized Approximate Newton Methods for Convex Optimization on Networked Systems
Hejie Wei, Zhihai Qu, Xuyang Wu +2
In this paper, a class of Decentralized Approximate Newton (DEAN) methods for addressing convex optimization on a networked system are developed, where nodes in the networked syste…
Fenchel Dual Gradient Methods for Distributed Convex Optimization over Time-varying Networks
Xuyang Wu, Jie Lu
In the large collection of existing distributed algorithms for convex multi-agent optimization, only a handful of them provide convergence rate guarantees on agent networks with ti…