research-article

Comparison of Several Kinds of Lagrangian Relaxation Algorithm Strategies for Solving Unit Combination Problems

Authors:

Yue Chen,

Jie DongAuthors Info & Claims

AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture

Pages 1834 - 1838

https://doi.org/10.1145/3495018.3495495

Published: 14 March 2022 Publication History

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Abstract

In order to find an algorithm with the highest efficiency for solving the unit combination problem, the characteristics of the unit combination problem are discussed in this paper. The advantages and disadvantages of the current mainstream methods for solving the unit combination problem are comprehensively analyzed, and it is concluded that the current methods for solving the unit combination problem are difficult to balance computational efficiency and computational accuracy. The Lagrangian relaxation method absorbs the constraints into the objective function by introducing Lagrangian multipliers to reduce the difficult constraints in the unit combination problem, which reduces the size of the unit combination problem and overcomes the problem of "dimensional disaster". The computational analysis of different number of unit combinations and the analysis of the pairwise gap also confirm that the Lagrangian relaxation method can better deal with the unit combination problem.

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Cited By

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Zhang XMa Z(2023)Research on UAV Passive Localization Based on Greedy Strategy and Two-degree Error Analysis Model2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology (ICCASIT)10.1109/ICCASIT58768.2023.10351663(57-63)Online publication date: 11-Oct-2023
https://doi.org/10.1109/ICCASIT58768.2023.10351663

Comparison of Several Kinds of Lagrangian Relaxation Algorithm Strategies for Solving Unit Combination Problems
1. Mathematics of computing
  1. Mathematical analysis
2. Theory of computation
  1. Design and analysis of algorithms

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AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture

October 2021

3136 pages

ISBN:9781450385046

DOI:10.1145/3495018

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 14 March 2022

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AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture

October 23 - 25, 2021

Manchester, United Kingdom

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Zhang XMa Z(2023)Research on UAV Passive Localization Based on Greedy Strategy and Two-degree Error Analysis Model2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology (ICCASIT)10.1109/ICCASIT58768.2023.10351663(57-63)Online publication date: 11-Oct-2023
https://doi.org/10.1109/ICCASIT58768.2023.10351663

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Successive Lagrangian relaxation algorithm for nonconvex quadratic optimization

The Lagrangian Relaxation Method for Solving Integer Programming Problems

The Lagrangian Relaxation Method for Solving Integer Programming Problems

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