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CONSENSUS OPTIMIZATION ON MANIFOLDS
 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 ..."
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Cited by 41 (8 self)
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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
 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 ..."
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Cited by 513 (18 self)
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T occurs. Faulttolerant 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
"... Distributed optimization algorithms are highly attractive for solving big data problems. In particular, many machine learning problems can be formulated as the global consensus optimization problem, which can then be solved in a distributed manner by the alternating direction method of multiplier ..."
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Cited by 5 (0 self)
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Distributed optimization algorithms are highly attractive for solving big data problems. In particular, many machine learning problems can be formulated as the global consensus optimization problem, which can then be solved in a distributed manner by the alternating direction method
On the linear convergence of the ADMM in decentralized consensus optimization
 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 ..."
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Cited by 12 (3 self)
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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
, 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 ..."
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Cited by 778 (6 self)
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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
 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 ..."
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Cited by 603 (20 self)
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' 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 highquality combiners
Consensus Optimizing Both Distance Sum and Radius
"... 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 consensus 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 ..."
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Cited by 3 (0 self)
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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 consensus 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
"... Probabilistic graphical models are powerful tools for analyzing constrained, continuous domains. However, finding mostprobable 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 ..."
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Cited by 17 (14 self)
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with piecewiselinear and piecewisequadratic dependencies and linear constraints over continuous domains. We derive algorithms based on a consensusoptimization framework and demonstrate their superior performance over state of the art. We show empirically that in a largescale voterpreference modeling problem
Fast linear iterations for distributed averaging.
 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 ..."
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Cited by 433 (12 self)
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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 HypergraphPartitioned Vertex Programming Approach for Largescale Consensus Optimization
"... In modern data science problems, techniques for extracting value from big data require performing largescale optimization over heterogenous, irregularly structured data. Much of this data is best represented as multirelational graphs, making vertexprogramming abstractions such as those of Pregel ..."
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Cited by 1 (1 self)
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of Pregel and GraphLab ideal fits for modern largescale data analysis. In this paper, we describe a vertexprogramming 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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