Displaying similar documents to “Distributed optimization for multi-agent system over unbalanced graphs with linear convergence rate”

Distributed aggregative optimization with quantized communication

Ziqin Chen, Shu Liang (2022)

Kybernetika

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In this paper, we focus on an aggregative optimization problem under the communication bottleneck. The aggregative optimization is to minimize the sum of local cost functions. Each cost function depends on not only local state variables but also the sum of functions of global state variables. The goal is to solve the aggregative optimization problem through distributed computation and local efficient communication over a network of agents without a central coordinator. Using the variable...

Distributed event-triggered algorithm for optimal resource allocation of multi-agent systems

Weiyong Yu, Zhenhua Deng, Hongbing Zhou, Xianlin Zeng (2017)

Kybernetika

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This paper is concerned with solving the distributed resource allocation optimization problem by multi-agent systems over undirected graphs. The optimization objective function is a sum of local cost functions associated to individual agents, and the optimization variable satisfies a global network resource constraint. The local cost function and the network resource are the private data for each agent, which are not shared with others. A novel gradient-based continuous-time algorithm...

A penalty ADMM with quantized communication for distributed optimization over multi-agent systems

Chenyang Liu, Xiaohua Dou, Yuan Fan, Songsong Cheng (2023)

Kybernetika

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In this paper, we design a distributed penalty ADMM algorithm with quantized communication to solve distributed convex optimization problems over multi-agent systems. Firstly, we introduce a quantization scheme that reduces the bandwidth limitation of multi-agent systems without requiring an encoder or decoder, unlike existing quantized algorithms. This scheme also minimizes the computation burden. Moreover, with the aid of the quantization design, we propose a quantized penalty ADMM...

High-performance simulation-based algorithms for an alpine ski racer's trajectory optimization in heterogeneous computer systems

Roman Dębski (2014)

International Journal of Applied Mathematics and Computer Science

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Effective, simulation-based trajectory optimization algorithms adapted to heterogeneous computers are studied with reference to the problem taken from alpine ski racing (the presented solution is probably the most general one published so far). The key idea behind these algorithms is to use a grid-based discretization scheme to transform the continuous optimization problem into a search problem over a specially constructed finite graph, and then to apply dynamic programming to find an...

Solving the Minimum Independent Domination Set Problem in Graphs by Exact Algorithm and Greedy Heuristic

Christian Laforest, Raksmey Phan (2013)

RAIRO - Operations Research - Recherche Opérationnelle

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In this paper we present a new approach to solve the Minimum Independent Dominating Set problem in general graphs which is one of the hardest optimization problem. We propose a method using a clique partition of the graph, partition that can be obtained greedily. We provide conditions under which our method has a better complexity than the complexity of the previously known algorithms. Based on our theoretical method, we design in the second part of this paper an efficient algorithm...

État de l'art des méthodes “d'optimisation globale”

Gérard Berthiau, Patrick Siarry (2010)

RAIRO - Operations Research

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We present a review of the main “global optimization" methods. The paper comprises one introduction and two parts. In the introduction, we recall some generalities about non linear constraint-less optimization and we list some classifications which have been proposed for the global optimization methods. We then describe, in the first part, various “classical" global optimization methods, most of which available long before the appearance of Simulated Annealing (a key event in this...

An improved nonmonotone adaptive trust region method

Yanqin Xue, Hongwei Liu, Zexian Liu (2019)

Applications of Mathematics

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Trust region methods are a class of effective iterative schemes in numerical optimization. In this paper, a new improved nonmonotone adaptive trust region method for solving unconstrained optimization problems is proposed. We construct an approximate model where the approximation to Hessian matrix is updated by the scaled memoryless BFGS update formula, and incorporate a nonmonotone technique with the new proposed adaptive trust region radius. The new ratio to adjusting the next trust...

Multi-agent solver for non-negative matrix factorization based on optimization

Zhipeng Tu, Weijian Li (2021)

Kybernetika

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This paper investigates a distributed solver for non-negative matrix factorization (NMF) over a multi-agent network. After reformulating the problem into the standard distributed optimization form, we design our distributed algorithm (DisNMF) based on the primal-dual method and in the form of multiplicative update rule. With the help of auxiliary functions, we provide monotonic convergence analysis. Furthermore, we show by computational complexity analysis and numerical examples that...

A factor graph based genetic algorithm

B. Hoda Helmi, Adel T. Rahmani, Martin Pelikan (2014)

International Journal of Applied Mathematics and Computer Science

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We propose a new linkage learning genetic algorithm called the Factor Graph based Genetic Algorithm (FGGA). In the FGGA, a factor graph is used to encode the underlying dependencies between variables of the problem. In order to learn the factor graph from a population of potential solutions, a symmetric non-negative matrix factorization is employed to factorize the matrix of pair-wise dependencies. To show the performance of the FGGA, encouraging experimental results on different separable...

Quantized cooperative output regulation of continuous-time multi-agent systems over switching graph

Ji Ma, Bo Yang, Jiayu Qiu, Ziqin Chen, Wenfeng Hu (2024)

Kybernetika

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This paper investigates the problem of quantized cooperative output regulation of linear multi-agent systems with switching graphs. A novel dynamic encoding-decoding scheme with a finite communication bandwidth is designed. Leveraging this scheme, a distributed protocol is proposed, ensuring asymptotic convergence of the tracking error under both bounded and unbounded link failure durations. Compared with the existing quantized control work of MASs, the semi-global assumption of initial...

A novel generalized oppositional biogeography-based optimization algorithm: application to peak to average power ratio reduction in OFDM systems

Sotirios K. Goudos (2016)

Open Mathematics

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A major drawback of orthogonal frequency division multiplexing (OFDM) signals is the high value of peak to average power ratio (PAPR). Partial transmit sequences (PTS) is a popular PAPR reduction method with good PAPR reduction performance, but its search complexity is high. In this paper, in order to reduce PTS search complexity we propose a new technique based on biogeography-based optimization (BBO). More specifically, we present a new Generalized Oppositional Biogeography Based Optimization...

Scheduling precedence task graphs with disturbances

Apurv Gupta, Gilles Parmentier, Denis Trystram (2003)

RAIRO - Operations Research - Recherche Opérationnelle

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In this paper we consider the problem of scheduling precedence task graphs in parallel processing where there can be disturbances in computation and communication times. Such a phenomenon often occurs in practice, due to our inability to exactly predict the time because of system intrusion like cache miss and packet transmission time in mediums like ethernet etc. We propose a method based on the addition of some extra edges to protect the initial scheduling from performing badly due...

Parallel machine scheduling with uncertain communication delays

Aziz Moukrim, Eric Sanlaville, Frédéric Guinand (2003)

RAIRO - Operations Research - Recherche Opérationnelle

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This paper is concerned with scheduling when the data are not fully known before the execution. In that case computing a complete schedule off-line with estimated data may lead to poor performances. Some flexibility must be added to the scheduling process. We propose to start from a partial schedule and to postpone the complete scheduling until execution, thus introducing what we call a stabilization scheme. This is applied to the m machine problem with communication delays: in our model...

A Global Stochastic Optimization Method for Large Scale Problems

W. El Alem, A. El Hami, R. Ellaia (2010)

Mathematical Modelling of Natural Phenomena

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In this paper, a new hybrid simulated annealing algorithm for constrained global optimization is proposed. We have developed a stochastic algorithm called ASAPSPSA that uses Adaptive Simulated Annealing algorithm (ASA). ASA is a series of modifications to the basic simulated annealing algorithm (SA) that gives the region containing the global solution of an objective function. In addition, Simultaneous Perturbation Stochastic Approximation...

Editorial

Clarisse Dhaenens, Patrick Siarry, El-Ghazali Talbi (2008)

RAIRO - Operations Research

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