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Use coupled LSTM networks to solve constrained optimization problems

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Title: Use coupled LSTM networks to solve constrained optimization problems
Authors: Chen, Z
Leung, KK
Wang, S
Tassiulas, L
Chan, K
Towsley, D
Item Type: Journal Article
Abstract: Gradient-based iterative algorithms have been widely used to solve optimization problems, including resource sharing and network management. When system parameters change, it requires a new solution independent of the previous parameter settings from the iterative methods. Therefore, we propose a learning approach that can quickly produce optimal solutions over a range of system parameters for constrained optimization problems. Two Coupled Long Short-Term Memory networks (CLSTMs) are proposed to find the optimal solution. The advantages of this framework include: (1) near-optimal solution for a given problem instance can be obtained in few iterations during the inference, (2) enhanced robustness as the CLSTMs can be trained using system parameters with distributions different from those used during inference to generate solutions. In this work, we analyze the relationship between minimizing the loss functions and solving the original constrained optimization problem for certain parameter settings. Extensive numerical experiments using datasets from Alibaba reveal that the solutions to a set of nonconvex optimization problems obtained by the CLSTMs reach within 90% or better of the corresponding optimum after 11 iterations, where the number of iterations and CPU time consumption are reduced by 81% and 33%, respectively, when compared with the gradient descent with momentum.
Issue Date: 13-Dec-2022
Date of Acceptance: 4-Dec-2022
URI: http://hdl.handle.net/10044/1/101476
DOI: 10.1109/tccn.2022.3228584
ISSN: 2332-7731
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 304
End Page: 316
Journal / Book Title: IEEE Transactions on Cognitive Communications and Networking
Volume: 9
Issue: 2
Copyright Statement: Copyright © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publication Status: Published
Online Publication Date: 2022-12-13
Appears in Collections:Computing
Faculty of Engineering