Papers Distributed Optimization
“Distributed Optimization” 태그가 달린 논문 601편 · 필터 해제
Asynchronous 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 OptimizationDistributed Stochastic Variance Reduced Gradient Methods and A Lower Bound for Communication Complexity
We study distributed optimization algorithms for minimizing the average of convex functions. The applications include empirical risk minimization problems in statistical machine learning where the datasets are large and …
Distributed OptimizationFast 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 OptimizationDUAL-LOCO: Distributing Statistical Estimation Using Random Projections
We present DUAL-LOCO, a communication-efficient algorithm for distributed statistical estimation. DUAL-LOCO assumes that the data is distributed according to the features rather than the samples. It requires only a singl…
Distributed OptimizationELM-Based Distributed Cooperative Learning Over Networks
This paper investigates distributed cooperative learning algorithms for data processing in a network setting. Specifically, the extreme learning machine (ELM) is introduced to train a set of data distributed across sever…
Cloud ComputingDistributed OptimizationA Hierarchical Approach for Joint Multi-view Object Pose Estimation and Categorization
We propose a joint object pose estimation and categorization approach which extracts information about object poses and categories from the object parts and compositions constructed at different layers of a hierarchical …
Distributed OptimizationObjectObject CategorizationPose EstimationAdding vs. Averaging in Distributed Primal-Dual Optimization
Distributed optimization methods for large-scale machine learning suffer from a communication bottleneck. It is difficult to reduce this bottleneck while still efficiently and accurately aggregating partial work from dif…
Distributed OptimizationCommunication-Efficient Distributed Optimization of Self-Concordant Empirical Loss
We consider distributed convex optimization problems originated from sample average approximation of stochastic optimization, or empirical risk minimization in machine learning. We assume that each machine in the distrib…
Binary ClassificationDistributed ComputingDistributed Optimizationregression+1Online Distributed Optimization on Dynamic Networks
This paper presents a distributed optimization scheme over a network of agents in the presence of cost uncertainties and over switching communication topologies. Inspired by recent advances in distributed convex optimiza…
Distributed OptimizationCommunication-Efficient Distributed Dual Coordinate Ascent
Communication remains the most significant bottleneck in the performance of distributed optimization algorithms for large-scale machine learning. In this paper, we propose a communication-efficient framework, CoCoA, that…
BIG-bench Machine LearningDistributed OptimizationHigh-performance Kernel Machines with Implicit Distributed Optimization and Randomization
In order to fully utilize "big data", it is often required to use "big models". Such models tend to grow with the complexity and size of the training data, and do not make strong parametric assumptions upfront on the nat…
Distributed OptimizationVocal Bursts Intensity PredictionROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images
Feature-based object matching is a fundamental problem for many applications in computer vision, such as object recognition, 3D reconstruction, tracking, and motion segmentation. In this work, we consider simultaneously …
3D ReconstructionDistributed OptimizationMotion SegmentationObject+2Asynchronous Forward Bounding for Distributed COPs
A new search algorithm for solving distributed constraint optimization problems (DisCOPs) is presented. Agents assign variables sequentially and compute bounds on partial assignments asynchronously. The asynchronous boun…
Distributed OptimizationCommunication Efficient Distributed Optimization using an Approximate Newton-type Method
We present a novel Newton-type method for distributed optimization, which is particularly well suited for stochastic optimization and learning problems. For quadratic objectives, the method enjoys a linear rate of conver…
Distributed OptimizationStochastic OptimizationVocal Bursts Type PredictionAsynchronous Adaptation and Learning over Networks - Part II: Performance Analysis
In Part I \cite{Zhao13TSPasync1}, we introduced a fairly general model for asynchronous events over adaptive networks including random topologies, random link failures, random data arrival times, and agents turning on an…
Distributed OptimizationAsynchronous Adaptation and Learning over Networks --- Part I: Modeling and Stability Analysis
In this work and the supporting Parts II [2] and III [3], we provide a rather detailed analysis of the stability and performance of asynchronous strategies for solving distributed optimization and adaptation problems ove…
Distributed OptimizationTrading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent
We present and study a distributed optimization algorithm by employing a stochastic dual coordinate ascent method. Stochastic dual coordinate ascent methods enjoy strong theoretical guarantees and often have better perf…
Distributed OptimizationCommunication/Computation Tradeoffs in Consensus-Based Distributed Optimization
We study the scalability of consensus-based distributed optimization algorithms by considering two questions: How many processors should we use for a given problem, and how often should they communicate when communicatio…
Distributed OptimizationMetric LearningDiffusion Adaptation over Networks
Adaptive networks are well-suited to perform decentralized information processing and optimization tasks and to model various types of self-organized and complex behavior encountered in nature. Adaptive networks consist …
Distributed OptimizationDistributed Delayed Stochastic Optimization
We analyze the convergence of gradient-based optimization algorithms whose updates depend on delayed stochastic gradient information. The main application of our results is to the development of distributed minimizatio…
Distributed OptimizationStochastic Optimization