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DP-ADMM: ADMM-Based Distributed Learning With Differential Privacy [PDF]
IEEE Transactions on Information Forensics and Security, 2020 Alternating Direction Method of Multipliers (ADMM) is a widely used tool for machine learning in distributed settings, where a machine learning model is trained over distributed data sources through an interactive process of local computation and message passing. Such an iterative process could cause privacy concerns of data owners.
Zonghao Huang +2 more
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DQC-ADMM: Decentralized Dynamic ADMM With Quantized and Censored Communications
IEEE Transactions on Neural Networks and Learning Systems, 2022In distributed learning and optimization, a network of multiple computing units coordinates to solve a large-scale problem. This article focuses on dynamic optimization over a decentralized network. We develop a communication-efficient algorithm based on the alternating direction method of multipliers (ADMM) with quantized and censored communications ...
Yaohua Liu +3 more
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An inexact accelerated stochastic ADMM for separable convex optimization
Computational Optimization and Applications, 2022 An inexact accelerated stochastic Alternating Direction Method of Multipliers (AS-ADMM) scheme is developed for solving structured separable convex optimization problems with linear constraints.
Jianchao Bai +2 more
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Privacy-Preserving Incremental ADMM for Decentralized Consensus Optimization
IEEE Transactions on Signal Processing, 2020 The alternating direction method of multipliers (ADMM) has been recently recognized as a promising optimizer for large-scale machine learning models. However, there are very few results studying ADMM from the aspect of communication costs, especially ...
Yu Ye, Hao Chen, Ming Xiao
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Benchmarking ADMM in nonconvex NLPs
Computers and Chemical Engineering, 2018 Abstract We study connections between the alternating direction method of multipliers (ADMM), the classical method of multipliers (MM), and progressive hedging (PH). The connections are used to derive benchmark metrics and strategies to monitor and accelerate convergence and to help explain why ADMM and PH are capable of solving complex nonconvex ...
Bethany Nicholson +2 more
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GPX-ADMM-Net: ADMM-based Neural Network with Generalized Proximal Operator
2020 28th European Signal Processing Conference (EUSIPCO), 2021In this paper, we propose a highly efficient and well interpretable deep learning solver, called Generalized ProXimal ADMM-Net (GPX-ADMM-Net), for the linear inverse problem, which is conventionally solved with intensive computations.GPX-ADMM-Net is characterized by the generalized proximal operator, convolutional dictionary, and modified loss function.
Shih-Wei Hu +2 more
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ADMM for consensus on colored networks
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012We propose a novel distributed algorithm for one of the most fundamental problems in networks: the average consensus. We view the average consensus as an optimization problem, which allows us to use recent techniques and results from the optimization area.
João F. C. Mota +3 more
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ADMM decoding on trapping sets
2015 IEEE International Symposium on Information Theory (ISIT), 2015Alternating direction method of multipliers (ADMM) decoding is a new decoding framework for low-density parity-check (LDPC) codes. It can be used to implement linear programming (LP) decoding or penalized LP decoding. Similar to belief propagation (BP) decoding, ADMM decoding consists of local “check” and “variable updates”.
Xishuo Liu, Stark C. Draper
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IEEE Communications Letters, 2018
Accurate estimation of time difference of arrival (TDOA) between sensors is a fundamental problem in various applications of sensor networks. In this letter, a novel algorithm is proposed to estimate TDOAs based on low-rank essence of a TDOA matrix, whose entry $(i,j)$ denotes TDOA value between sensors $i$ and $j$ with respect to a ...
Yanping Zhu +5 more
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Accurate estimation of time difference of arrival (TDOA) between sensors is a fundamental problem in various applications of sensor networks. In this letter, a novel algorithm is proposed to estimate TDOAs based on low-rank essence of a TDOA matrix, whose entry $(i,j)$ denotes TDOA value between sensors $i$ and $j$ with respect to a ...
Yanping Zhu +5 more
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Solving Lasso: Extended ADMM is more efficient than ADMM
2015 Chinese Automation Congress (CAC), 2015The least absolute shrinkage and selection operator (Lasso) has become very popular and attractive approach for regularization and variable selection for high-dimensional data in machine learning. In this paper, we present an extended alternating direction method of multipliers (ADMM) for solving the Lasso.
null Feng Ma +3 more
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