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Variance reduction technology-based distributed projection method considering communication delay

A distributed and projection algorithm technology, applied in the field of intelligent communication, can solve the problems of large gradient calculation, low efficiency of calculation and communication of multi-agent system, and heavy burden of intelligent agent calculation.

Pending Publication Date: 2020-12-11
SOUTHWEST UNIVERSITY
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Problems solved by technology

However, when the existing distributed optimization algorithms face large-scale convex optimization problems with relatively complex local constraints, the amount of gradient calculation is large, and the calculation burden of the agents in the network is heavy, which leads to the calculation and communication of multi-agent systems. Inefficiency and other deficiencies, so it cannot meet people's requirements

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  • Variance reduction technology-based distributed projection method considering communication delay
  • Variance reduction technology-based distributed projection method considering communication delay
  • Variance reduction technology-based distributed projection method considering communication delay

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specific Embodiment approach

[0100] Secondly the specific embodiment of the present invention is as follows:

[0101] A distributed projection method based on variance reduction technology considering communication delay, comprising the following steps:

[0102] Step 1. Propose an original optimization problem model (1) for a multi-intelligent system with both local set constraints and local equality constraints;

[0103] Step 2, the original optimization problem model (1) obtained in step 1 is converted into a convex optimization problem model (2) that is convenient for distribution processing;

[0104] Step 3. Propose a distributed projection algorithm based on variance reduction technology (3) to solve the constrained convex optimization problem model (2), that is, use local stochastic average gradient to unbiasedly estimate the local full gradient, so as to reduce the problem in each iteration The heavy computational burden caused by calculating the full gradient of all local objective functions in ;...

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Abstract

The invention discloses a variance reduction technology-based distributed projection method considering communication time delay. The method comprises the following steps of 1, proposing an original optimization problem model (1) for a multi-intelligent system with local set constraints and local equation constraints at the same time; 2, equivalently converting the original optimization problem model (1) obtained in the step 1 into a convex optimization problem model (2) convenient for distribution processing; step 3, proposing a variance reduction technology-based distributed projection algorithm (3), solving a constrained convex optimization problem model (2), i.e., estimating a local full gradient in an unbiased manner by adopting a local random average gradient so as to reduce a heavycalculation burden caused by calculating full gradients of all local target functions in each iteration; and 4, performing convergence analysis. According to the invention, the calculation cost of allagents in the network can be greatly reduced, so that the communication and calculation pressure of the whole multi-agent system is reduced, and the practicability is relatively high.

Description

technical field [0001] The present invention relates to the technical field of intelligent communication, in particular to a distributed projection method based on variance reduction technology considering communication time delay. Background technique [0002] In recent years, with the vigorous development of high technology, especially the emergence of emerging fields such as cloud computing and big data. Distributed optimization theory and applications have received more and more attention, and gradually penetrated into all aspects of scientific research, engineering applications, and social life. Distributed optimization is effectively optimized through the cooperation and coordination among multiple agents. It can be used to solve large-scale and complex optimization problems that are difficult for many centralized algorithms. However, when the existing distributed optimization algorithms face large-scale convex optimization problems with relatively complex local const...

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Application Information

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IPC IPC(8): G06F30/18G06F30/20G06F111/04
CPCG06F30/18G06F30/20G06F2111/04
Inventor 李华青胡锦辉夏大文陈欣王政吕庆国黄廷文
Owner SOUTHWEST UNIVERSITY
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