新しい降下構造を持つ3項共役勾配法と低炭素サプライチェーン最適化への応用
A three-term conjugate gradient method with a new descent structure for unconstrained optimization and low-carbon supply chain optimization (原題)
Maulana Malik, Sulaiman Mohammed Ibrahim, Dian Lestari, Fevi Novkaniza, S. Devila, F F Addini, Gladwin Gunawan
🤖 gxceed AI 要約
日本語
本論文は、無制約最適化のための改良型3項共役勾配法を提案し、強いWolfe直線探索の下で十分な降下条件と大域的収束性を数学的に証明する。数値実験で既存手法より優れた性能を示し、さらに低炭素サプライチェーン最適化問題への応用可能性を示す。
English
This paper proposes an improved three-term conjugate gradient method for unconstrained optimization, proving sufficient descent and global convergence under strong Wolfe line search. Numerical experiments show superiority over existing methods, and the method is extended to a low-carbon supply chain optimization problem as a real-world application.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では、サプライチェーン排出量削減がSSBJ開示やScope 3対応で重要課題となっており、最適化手法の応用は企業の排出削減計画に役立つ可能性がある。ただし、本論文の主眼は数学的手法であり、日本の政策や制度との直接的な関連は薄い。
In the global GX context
Globally, low-carbon supply chain optimization is relevant to Scope 3 emissions management and climate disclosure frameworks like CDP and ISSB. However, the paper's primary contribution is mathematical, with the supply chain application being illustrative rather than empirically grounded.
👥 読者別の含意
🔬研究者:Optimization researchers can evaluate the proposed CG method's theoretical properties and potential for further applications.
🏢実務担当者:Supply chain managers may find the method's potential for emissions reduction, but practical implementation requires further validation.
📄 Abstract（原文）
Optimization methods play a vital role in solving large-scale problems across engineering and environmental fields. Among them, the conjugate gradient (CG) method is popular for its computational efficiency, and recent developments—such as the three-term CG variant—have shown improved convergence and numerical stability. This paper proposes an improved three-term CG method for unconstrained optimization. Inspired by the Three-Term Zheng-Huang-Shi (ZHS) CG parameterization and Three-Term Rivaie-Mustafa-Ismail-Leong (TTRMIL), this method is specifically designed to improve the computational performance of CG methods. In the proposed method, sufficient descent conditions and global convergence properties for general functions are mathematically proven under assumption and criteria from the strong Wolfe line search. Numerical experiments conducted on several unconstrained optimization problems highlight the superiority of the new method over certain CG methods with similar characteristics. In real-world applications, the proposed method is extended to address the challenge of reducing carbon emissions for sustainability, particularly within the Low Carbon Supply Chain (LCSC) optimization problem.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1371/journal.pone.0356498first seen 2026-08-29 04:41:07
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