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Distributed Partitioned Big-Data Optimization via asynchronous Dual Decomposition

IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2018年第4期5卷 1910-1919页

作者： Notarnicola, Ivano Carli, Ruggero Notarstefano, Giuseppe Univ Salento Dept Engn I-73100 Lecce Italy Univ Padua Dept Informat Engn I-35131 Padua Italy

In this paper, we consider a novel partitioned framework for distributed optimization in peer-to-peer networks. In several important applications, the agents of a network have to solve an optimization problem with two key features: i) the dimension of the decision variable depends on the network size, and ii) cost function and constraints have a sparsity structure related to the communication graph. For this class of problems, a straightforward application of existing consensus methods would show two inefficiencies: poor scalability and redundancy of shared information. We propose an asynchronous distributed algorithm, based on dual decomposition and coordinate methods, to solve partitioned optimization problems. We show that by exploiting the problem structure, the solution can be partitioned among the nodes, so that each node just stores a local copy of a portion of the decision variable (rather than a copy of the entire decision vector) and solves a small-scale local problem.

关键词： asynchronous algorithms big-data optimization distributed algorithms distributed optimization optimization methods

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asynchronous Distributed Optimization Via Randomized Dual Proximal Gradient

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2017年第5期62卷 2095-2106页

作者： Notarnicola, Ivano Notarstefano, Giuseppe Univ Salento Dept Engn Lecce Italy

In this paper we consider distributed optimization problems in which the cost function is separable, i.e., a sum of possibly non-smooth functions all sharing a common variable, and can be split into a strongly convex term and a convex one. The second term is typically used to encode constraints or to regularize the solution. We propose a class of distributed optimization algorithms based on proximal gradientmethods applied to the dual problem. We show that, by choosing suitable primal variable copies, the dual problem is itself separable when written in terms of conjugate functions, and the dual variables can be stacked into non-overlapping blocks associated to the computing nodes. We first show that a weighted proximal gradient on the dual function leads to a synchronous distributed algorithm with local dual proximal gradient updates at each node. Then, as main paper contribution, we develop asynchronous versions of the algorithm in which the node updates are triggered by local timers without any global iteration counter. The algorithms are shown to be proper randomized blockcoordinate proximal gradient updates on the dual function.

关键词： Distributed optimization large-scale optimization asynchronous algorithms constrained LASSO

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Superlinear convergence of asynchronous multi-splitting waveform relaxation methods applied to a system of nonlinear ordinary differential equations

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MATHEMATICS AND COMPUTERS IN SIMULATION 2008年第2-3期77卷 179-188页

作者： El-Kyal, M. Machmoum, A. Univ Ibn Zohr ENSA LIP2E Agadir Morocco Univ Ibn Zohr Fac Sci LAMA Agadir Morocco

We prove the superlinear convergence of asynchronous multi-splitting waveform relaxation (MSWR) methods applied to a system of nonlinear ordinary differential equations. This study is based on the technique of nested sets. It allows to specify the class of the convergence. (c) 2007 IMACS. Published by Elsevier B.V. All rights reserved.

关键词： numerical analysis waveform relaxation methods multi-splitting asynchronous algorithms

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asynchronous and implicitly parallel evolutionary computation models

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SOFT COMPUTING 2014年第6期18卷 1225-1236页

作者： Jakobovic, Domagoj Golub, Marin Cupic, Marko Univ Zagreb Fac Elect Engn & Comp Zagreb 41000 Croatia

This paper presents the design and the application of asynchronous models of parallel evolutionary algorithms. An overview of the existing parallel evolutionary algorithm (PEA) models and available implementations is given. We present new PEA models in the form of asynchronous algorithms and implicit parallelization, as well as experimental data on their efficiency. The paper also discusses the definition of speedup in PEAs and proposes an appropriate speedup measurement procedure. The described parallel EA algorithms are tested on problems with varying degrees of computational complexity. The results show good efficiency of asynchronous and implicit models compared to existing parallel algorithms.

关键词： Evolutionary algorithms Parallelization asynchronous algorithms

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AN asynchronous DISTRIBUTED APPROACH FOR THE SIMULATION OF BEHAVIOR-LEVEL MODELS ON PARALLEL PROCESSORS

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 1995年第6期6卷 639-652页

作者： GHOSH, S YU, ML AT&T BELL LABS HOLMDELNJ 07733

Traditional approaches to the distributed simulation of digital designs are limited in that they are inefficient and prone to deadlock for systems with feedback loops. This paper proposes an asynchronous distributed algorithm to the simulation and verification of behavior-level models and describes its implementation on an actual loosely-coupled parallel processor, The approach is relatively efficient for realistic digital designs and mathematically shown to be deadlock free, Additionally, it includes a new technique [6] for modeling the timing of digital systems that guarantees the accuracy of the simulation results, In the discipline of computer-aided design of digital systems, a behavior model refers to a compact and executable representation of the activities of a complex digital component or an entire digital system such as the Motorola 68000, expressed through a high-level hardware description language such as ADLIB [2] or VHDL [1]. Behavior models are popular be;cause of their flexibility;however, they execute excruciatingly slow on uniprocessor computers, The approach presented in this paper has the potential to significantly reduce the execution time through concurrent execution on multiple processors, In this approach, a design is first partitioned by the user and the behavior models corresponding to the digital components of each partition are assigned to a unique processor of the parallel processor system. In course of execution, a behavior model receives signal transitions from other models through the explicit communication links, termed protocols, that model the connectivity of the digital design, Every model is completely unaware of the existence of other entities in the simulation system and is solely responsible for accurately scheduling its execution on the underlying processor, Thus, scheduling is distributed in the models and the approach, like the Chandy-Misra algorithm [11], does not require any knowledge of the global simulation time, However

关键词： DISTRIBUTED SIMULATION DIGITAL SYSTEMS SIMULATION asynchronous algorithms PARALLEL PROCESSORS

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A distributed asynchronous method of multipliers for constrained nonconvex optimization

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AUTOMATICA 2019年 103卷 243-253页

作者： Farina, Francesco Garulli, Andrea Giannitrapani, Antonio Notarstefano, Giuseppe Univ Siena Dipartimento Ingn Informaz & Sci Matemat Siena Italy Univ Bologna Dept Elect Elect & Informat Engn G Marconi Bologna Italy

This paper presents a fully asynchronous and distributed approach for tackling optimization problems in which both the objective function and the constraints may be nonconvex. In the considered network setting each node is active upon triggering of a local timer and has access only to a portion of the objective function and to a subset of the constraints. In the proposed technique, based on the method of multipliers, each node performs, when it wakes up, either a descent step on a local augmented Lagrangian or an ascent step on the local multiplier vector. Nodes realize when to switch from the descent step to the ascent one through an asynchronous distributed logic-AND, which detects when all the nodes have reached a predefined tolerance in the minimization of the augmented Lagrangian. It is shown that the resulting distributed algorithm is equivalent to a block coordinate descent for the minimization of the global augmented Lagrangian. This allows one to extend the properties of the centralized method of multipliers to the considered distributed framework. Two application examples are presented to validate the proposed approach: a distributed source localization problem and the parameter estimation of a neural network. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Distributed optimization Multi-agent systems Method of multipliers asynchronous algorithms

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asynchronous Optimization Over Heterogeneous Networks Via Consensus ADMM

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2017年第1期3卷 114-129页

作者： Kumar, Sandeep Jain, Rahul Rajawat, Ketan Indian Inst Technol Dept Elect Engn Kanpur 208016 Uttar Pradesh India Qualcomm India Pvt Ltd Bangalore 560066 Karnataka India

This paper considers the distributed optimization of a sum of locally observable, nonconvex functions. The optimization is performed over a multiagent networked system, and each local function depends only on a subset of the variables. An asynchronous and distributed alternating directions method of multipliers (ADMM) method that allows the nodes to defer or skip the computation and transmission of updates is proposed in the paper. The proposed algorithm utilizes different approximations in the update step, resulting in proximal and majorized ADMM variants. Both variants are shown to converge to a local minimum under certain regularity conditions. The proposed asynchronous algorithms are also applied to the problem of cooperative localization in wireless ad hoc networks, where it is shown to outperform the other state-of-the-art localization algorithms.

关键词： ADMM asynchronous algorithms distributed optimization majorization non-convex problems

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DECK: A distributed, asynchronous and exact k-connectivity detection algorithm for Wireless Sensor Networks

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COMPUTER COMMUNICATIONS 2018年 116卷 9-20页

作者： Akram, Vahid Khalilpour Dagdeviren, Orhan Ege Univ Int Comp Inst Izmir Turkey

Wireless Sensor Networks (WSNs) are one of the widespread platforms for communications and remote sensing. A robust WSN should tolerate the failures of nodes without losing the connection to active nodes. A network is k-connected if all active nodes remain connected after failures in k-1 arbitrary nodes. Finding (detecting) the k value in a WSN is a significant operation to estimate the connectivity robustness, reliability and load balancing level of the network. Also, the detection of k values provides useful information for connectivity restoration, lower bound of node degree, critical nodes and possible cycles. In this paper, we propose an asynchronous distributed algorithm (DECK) for k-connectivity detection in WSNs. In the proposed algorithm, each node estimates a local k using its 2-hop neighborhood information and then a distributed linked list of minimum estimations is created between the nodes. Finally, the sink node validates the correctness of detected values by finding the number of node-disjoint paths to the node having the minimum estimation. We analyze our algorithm in detail, provide theoretical analysis, testbed experiments on the IRIS nodes and simulation results in the TOSSIM simulator by comparing with the other algorithms. The comprehensive testbed and simulation results show that the proposed algorithm always finds exact k values with reasonable energy consumption while the correct detection ratios of existing distributed algorithms on similar networks are usually less than 40%.

关键词： Wireless Sensor Networks Fault tolerance k-connectivity detection Distributed algorithms asynchronous algorithms

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asynchronous Optimization Over Graphs: Linear Convergence Under Error Bound Conditions

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第10期66卷 4604-4619页

作者： Cannelli, Loris Facchinei, Francisco Scutari, Gesualdo Kungurtsev, Vyacheslav Ist Dalle Molle Intelligenza Artificiale IDSIA USI SUPSI CH-6900 Lugano Switzerland Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA Univ Roma La Sapienza Dept Comp Control Arid Management Engn I-00185 Rome Italy Czech Tech Univ Dept Comp Sci Prague 16636 Czech Republic

We consider convex and nonconvex constrained optimization with a partially separable objective function: Agents minimize the sum of local objective functions, each of which is known only by the associated agent and depends on the variables of that agent and those of a few others. This partitioned setting arises in several applications of practical interest. We propose what is, to the best of our knowledge, the first distributed, asynchronous algorithm with rate guarantees for this class of problems. When the objective function is nonconvex, the algorithm provably converges to a stationary solution at a sublinear rate whereas linear rate is achieved under the renowned Luo-Tseng error bound condition (which is less stringent than strong convexity). Numerical results on matrix completion and LASSO problems show the effectiveness of our method.

关键词： Delays Optimization Convergence Partitioning algorithms Nickel Linear programming Indexes asynchronous algorithms error bounds linear rate multiagent systems nonconvex optimization

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Synchronous and asynchronous HEVC parallel encoder versions based on a GOP approach

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ADVANCES IN ENGINEERING SOFTWARE 2016年 101卷 37-49页

作者： Migallon, H. Hernandez-Losada, J. L. Cebrian-Marquez, G. Pinol, P. Martinez, J. L. Lopez-Granado, O. Malumbres, M. P. Univ Miguel Hernandez Dept Phys & Comp Architecture E-03202 Alicante Spain Univ Castilla La Mancha Albacete Res Inst Informat E-02071 Albacete Spain

In this paper, we focus on applying parallel processing techniques to HEVC encoder in order to significantly reduce the computational power requirements without disturbing its coding efficiency. So, we propose several, synchronous and asynchronous, parallelization approaches working at a coarse grain parallelization level, based on the Group Of Pictures (GOP), which we call GOP-based level. GOP -based approaches encode simultaneously several groups of consecutive frames. Depending on how these GOPs are conformed and distributed it is critical to obtain good parallel performance. The results show that near ideal efficiencies are obtained using up to 10 cores. Furthermore, when the computational load is unbalanced, the asynchronous versions outperform the synchronous ones. The parallel algorithms developed in this work support all standard coding modes proposed by the reference software. (C) 2016 Civil-Comp Ltd. and Elseider Ltd. All rights reserved.

关键词： Parallel algorithms Video coding HEVC Multicore Performance GOP-based algorithms Synchronous algorithms asynchronous algorithms

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