Adaptive Consensus ADMM for Distributed Optimization
The alternating direction method of multipliers (ADMM) is commonly used for distributed model fitting problems, but its performance and reliability depend strongly on user-defined penalty parameters. We study distributed ADMM methods that boost performance by using different fine-tuned algorithm parameters on each worker node. We present a O(1/k) convergence rate for adaptive ADMM methods with node-specific parameters, and propose adaptive consensus ADMM (ACADMM), which automatically tunes parameters without user oversight.
Code (0)
등록된 구현이 없습니다.
Tasks
Distributed OptimizationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty
We propose new methods to speed up convergence of the Alternating Direction Method of Multipliers (ADMM), a common optimization tool in the context of large scale and distributed learning. The proposed method accelerates…
Distributed OptimizationA Distributed Algorithm for Measure-valued Optimization with Additive Objective
We propose a distributed nonparametric algorithm for solving measure-valued optimization problems with additive objectives. Such problems arise in several contexts in stochastic learning and control including Langevin sa…
A Block-wise, Asynchronous and Distributed ADMM Algorithm for General Form Consensus Optimization
Many machine learning models, including those with non-smooth regularizers, can be formulated as consensus optimization problems, which can be solved by the alternating direction method of multipliers (ADMM). Many recent…
FormAsynchronous Distributed ADMM for Large-Scale Optimization- Part I: Algorithm and Convergence Analysis
Aiming at solving large-scale learning problems, this paper studies distributed optimization methods based on the alternating direction method of multipliers (ADMM). By formulating the learning problem as a consensus pro…
Distributed OptimizationAsynchronous Distributed Multi-Robot Motion Planning Under Imperfect Communication
This paper addresses the challenge of coordinating multi-robot systems under realistic communication delays using distributed optimization. We focus on consensus ADMM as a scalable framework for generating collision-free…
Distributed OptimizationMotion Planning