2023
DOI: 10.3390/math12010043
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An Adaptive Low Computational Cost Alternating Direction Method of Multiplier for RELM Large-Scale Distributed Optimization

Ke Wang,
Shanshan Huo,
Banteng Liu
et al.

Abstract: In a class of large-scale distributed optimization, the calculation of RELM based on the Moore–Penrose inverse matrix is prohibitively expensive, which hinders the formulation of a computationally efficient optimization model. Attempting to improve the model’s convergence performance, this paper proposes a low computing cost Alternating Direction Method of Multipliers (ADMM), where the original update in ADMM is solved inexactly with approximate curvature information. Based on quasi-Newton techniques, the ADMM… Show more

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(1 citation statement)
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“…Experimental results demonstrated its superior generalization performance compared to traditional ELMs and other state-of-the-art ensemble ELMs. Finally, Wang et al [97] introduced a computationally efficient alternating direction method of multipliers (ADMM) with approximate curvature information for solving the update in ADMM inexactly. This algorithm, when applied to the RELM model, decomposes the model fitting problem into parallelizable subproblems, enhancing classification efficiency.…”
Section: Other Tools and Technologies For Distributed And Parallel Co...mentioning
confidence: 99%
“…Experimental results demonstrated its superior generalization performance compared to traditional ELMs and other state-of-the-art ensemble ELMs. Finally, Wang et al [97] introduced a computationally efficient alternating direction method of multipliers (ADMM) with approximate curvature information for solving the update in ADMM inexactly. This algorithm, when applied to the RELM model, decomposes the model fitting problem into parallelizable subproblems, enhancing classification efficiency.…”
Section: Other Tools and Technologies For Distributed And Parallel Co...mentioning
confidence: 99%