A discussion on tighter estimate of the duality gap

This paper reviews a key concept in optimization theory, the duality gap, and takes classical unit commitment and economic dispatch problems in power systems as instances to explain its cause. Then, the paper explains the principle of estimating the duality gap in Reference [1] and the inaccuracy that exists. The paper innovatively proposes the idea of using the number of binding constraints to estimate the relative duality gap that expands the current theory. Finally, the paper uses randomly generated computational instances to demonstrate the correctness of the proposed estimation method. A rigorous proof will be provided separately.

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Publication Details

Journal
IET conference proceedings.
Published
2025-03-01
DOI
https://doi.org/10.1049/icp.2025.0382
Primary Topic
Matrix Theory and Algorithms
Type
article
Field-Weighted Citation Impact
0.00
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article

A discussion on tighter estimate of the duality gap

Feng Gao, Jiami Sun, Zhitong Yu, Chen Shen
IET conference proceedings.
Matrix Theory and Algorithms
article

A discussion on tighter estimate of the duality gap

Feng Gao, Jiami Sun, Zhitong Yu, Chen Shen
article en

Abstract

This paper reviews a key concept in optimization theory, the duality gap, and takes classical unit commitment and economic dispatch problems in power systems as instances to explain its cause. Then, the paper explains the principle of estimating the duality gap in Reference [1] and the inaccuracy that exists. The paper innovatively proposes the idea of using the number of binding constraints to estimate the relative duality gap that expands the current theory. Finally, the paper uses randomly generated computational instances to demonstrate the correctness of the proposed estimation method. A rigorous proof will be provided separately.

IET conference proceedings.Vol. 2024(33)
China National Space Administration (CN), Tsinghua Sichuan Energy Internet Research Institute (CN), Tsinghua University (CN)
Reduced inequalities
Openalex Percentile: Top 94%
Matrix Theory and Algorithms
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