Displaying similar documents to “Distributed aggregative optimization with quantized communication”

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...

Scheduling UET Trees with Communication Delays on two Processors

Frederic Guinand, Denis Trystman (2010)

RAIRO - Operations Research

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In this paper, we present a new linear time algorithm for scheduling UECT (Unit Execution and Communication Time) trees on two identical processors. The chosen criterion is the makespan. The used strategy is based on clustering of tasks. We show that this algorithm builds optimal schedules. Some extensions are discussed for non UECT tasks.

Minimization of communication expenditure for seasonal products

Igor Bykadorov, Andrea Ellero, Elena Moretti (2002)

RAIRO - Operations Research - Recherche Opérationnelle

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We consider a firm that sells seasonal goods. The firm seeks to reach a fixed level of goodwill at the end of the selling period, with the minimum total expenditure in promotional activities. We consider the linear optimal control problem faced by the firm which can only control the communication expenditure rate; communication is performed by means of advertising and sales promotion. Goodwill and sales levels are considered as state variables and word-of-mouth effect and saturation...

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...

Parallel dynamic programming algorithms: Multitransputer systems

Jan Sadecki (2002)

International Journal of Applied Mathematics and Computer Science

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The present paper discusses real parallel computations. On the basis of a selected group of dynamic programming algorithms, a number of factors affecting the efficiency of parallel computations such as, e.g., the way of distributing tasks, the interconnection structure between particular elements of the parallel system or the way of organizing of interprocessor communication are analyzed. Computations were implemented in the parallel multitransputer SUPER NODE 1000 system using from...

Parallel Machine Scheduling with Uncertain Communication Delays

Aziz Moukrim, Eric Sanlaville, Frédéric Guinand (2010)

RAIRO - Operations Research

Similarity:

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...

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...