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CONSENSUS OPTIMIZATION ON MANIFOLDS

by Alain Sarlette, Rodolphe Sepulchre - VOL. 48, NO. 1, PP. 56–76 C ○ 2009 SOCIETY FOR INDUSTRIAL AND APPLIED MATHEMATICS , 2009
"... The present paper considers distributed consensus algorithms that involve N agents evolving on a connected compact homogeneous manifold. The agents track no external reference and communicate their relative state according to a communication graph. The consensus problem is formulated in terms of th ..."
Abstract - Cited by 41 (8 self) - Add to MetaCart
The present paper considers distributed consensus algorithms that involve N agents evolving on a connected compact homogeneous manifold. The agents track no external reference and communicate their relative state according to a communication graph. The consensus problem is formulated in terms

Consensus in the presence of partial synchrony

by Cynthia Dwork, Nancy Lynch, Larry Stockmeyer - JOURNAL OF THE ACM , 1988
"... The concept of partial synchrony in a distributed system is introduced. Partial synchrony lies between the cases of a synchronous system and an asynchronous system. In a synchronous system, there is a known fixed upper bound A on the time required for a message to be sent from one processor to ano ..."
Abstract - Cited by 513 (18 self) - Add to MetaCart
T occurs. Fault-tolerant consensus protocols are given for various cases of partial synchrony and various fault models. Lower bounds that show in most cases that our protocols are optimal with respect to the number of faults tolerated are also given. Our consensus protocols for partially synchronous

Asynchronous Distributed ADMM for Consensus Optimization

by Ruiliang Zhang, James T. Kwok
"... Distributed optimization algorithms are highly attractive for solving big data problems. In par-ticular, many machine learning problems can be formulated as the global consensus opti-mization problem, which can then be solved in a distributed manner by the alternating direc-tion method of multiplier ..."
Abstract - Cited by 5 (0 self) - Add to MetaCart
Distributed optimization algorithms are highly attractive for solving big data problems. In par-ticular, many machine learning problems can be formulated as the global consensus opti-mization problem, which can then be solved in a distributed manner by the alternating direc-tion method

On the linear convergence of the ADMM in decentralized consensus optimization

by Wei Shi, Qing Ling, Kun Yuan, Gang Wu, Wotao Yin - IEEE Transactions on Signal Processing , 2014
"... Abstract—In decentralized consensus optimization, a connected network of agents collaboratively minimize the sum of their local objective functions over a common decision variable, where their information exchange is restricted between the neighbors. To this end, one can first obtain a problem refor ..."
Abstract - Cited by 12 (3 self) - Add to MetaCart
Abstract—In decentralized consensus optimization, a connected network of agents collaboratively minimize the sum of their local objective functions over a common decision variable, where their information exchange is restricted between the neighbors. To this end, one can first obtain a problem

A New Extension of the Kalman Filter to Nonlinear Systems

by Simon J. Julier, Jeffrey K. Uhlmann , 1997
"... The Kalman filter(KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be difficult. The most common approach is to use the Extended Kalman Filter (EKF) which ..."
Abstract - Cited by 778 (6 self) - Add to MetaCart
The Kalman filter(KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be difficult. The most common approach is to use the Extended Kalman Filter (EKF

Cluster Ensembles - A Knowledge Reuse Framework for Combining Multiple Partitions

by Alexander Strehl, Joydeep Ghosh, Claire Cardie - Journal of Machine Learning Research , 2002
"... This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings. We first identify several application scenarios for the resultant 'knowledge reuse&ap ..."
Abstract - Cited by 603 (20 self) - Add to MetaCart
' framework that we call cluster ensembles. The cluster ensemble problem is then formalized as a combinatorial optimization problem in terms of shared mutual information. In addition to a direct maximization approach, we propose three effective and efficient techniques for obtaining high-quality combiners

Consensus Optimizing Both Distance Sum and Radius

by Amihood Amir, Gad M. L, Joong Chae Na, Heejin Park, Kunsoo Park, Jeong Seop Sim
"... Abstract. The consensus string problem is finding a representative string (consensus) of a given set S of strings. In this paper we deal with the con-sensus string problems optimizing both distance sum and radius, where the distance sum is the sum of (Hamming) distances from the strings in S to the ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
Abstract. The consensus string problem is finding a representative string (consensus) of a given set S of strings. In this paper we deal with the con-sensus string problems optimizing both distance sum and radius, where the distance sum is the sum of (Hamming) distances from the strings

Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization

by Stephen H. Bach, Lise Getoor, Matthias Broecheler, Dianne P. O’leary
"... Probabilistic graphical models are powerful tools for analyzing constrained, continuous domains. However, finding most-probable explanations (MPEs) in these models can be computationally expensive. In this paper, we improve the scalability of MPE inference in a class of graphical models with piecewi ..."
Abstract - Cited by 17 (14 self) - Add to MetaCart
with piecewise-linear and piecewise-quadratic dependencies and linear constraints over continuous domains. We derive algorithms based on a consensus-optimization framework and demonstrate their superior performance over state of the art. We show empirically that in a large-scale voter-preference modeling problem

Fast linear iterations for distributed averaging.

by Lin Xiao , Stephen Boyd - Systems & Control Letters, , 2004
"... Abstract We consider the problem of finding a linear iteration that yields distributed averaging consensus over a network, i.e., that asymptotically computes the average of some initial values given at the nodes. When the iteration is assumed symmetric, the problem of finding the fastest converging ..."
Abstract - Cited by 433 (12 self) - Add to MetaCart
Abstract We consider the problem of finding a linear iteration that yields distributed averaging consensus over a network, i.e., that asymptotically computes the average of some initial values given at the nodes. When the iteration is assumed symmetric, the problem of finding the fastest

A Hypergraph-Partitioned Vertex Programming Approach for Large-scale Consensus Optimization

by Hui Miao, Xiangyang Liu, Bert Huang, Lise Getoor
"... In modern data science problems, techniques for extracting value from big data require performing large-scale optimization over heterogenous, irregularly structured data. Much of this data is best represented as multi-relational graphs, making vertex-programming abstractions such as those of Pregel ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
of Pregel and GraphLab ideal fits for modern large-scale data analysis. In this paper, we describe a vertex-programming implementation of a popular consensus optimization technique known as the alternating direction method of multipliers (ADMM) [1]. ADMM consensus optimization allows the elegant solution
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