A one-layer recurrent neural network for robust linear programming subject to l∞ norm uncertainty
Robust optimization problems subject to norm uncertainty appear in numerous applications in various fields such as engineering, logistics, and finance. Despite its importance, robust optimization algorithms face significant computational challenges for solving high-dimensional problems, limiting the...
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| Veröffentlicht in: | Neural networks Jg. 194; S. 108144 |
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| Sprache: | Englisch |
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01.02.2026
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| ISSN: | 0893-6080, 1879-2782, 1879-2782 |
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| Abstract | Robust optimization problems subject to norm uncertainty appear in numerous applications in various fields such as engineering, logistics, and finance. Despite its importance, robust optimization algorithms face significant computational challenges for solving high-dimensional problems, limiting their practical use. This paper presents a neurodynamic approach to mitigate these challenges by transforming the robust linear programming to a non-smooth convex optimization through parameter elimination. A one-layer projection neural network with proven stability and convergence is proposed to solve the non-smooth optimization problem. The effectiveness of this approach is validated based on simulations of numerical examples and applications in reactor design and wastewater treatment. |
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| AbstractList | Robust optimization problems subject to norm uncertainty appear in numerous applications in various fields such as engineering, logistics, and finance. Despite its importance, robust optimization algorithms face significant computational challenges for solving high-dimensional problems, limiting their practical use. This paper presents a neurodynamic approach to mitigate these challenges by transforming the robust linear programming to a non-smooth convex optimization through parameter elimination. A one-layer projection neural network with proven stability and convergence is proposed to solve the non-smooth optimization problem. The effectiveness of this approach is validated based on simulations of numerical examples and applications in reactor design and wastewater treatment. Robust optimization problems subject to norm uncertainty appear in numerous applications in various fields such as engineering, logistics, and finance. Despite its importance, robust optimization algorithms face significant computational challenges for solving high-dimensional problems, limiting their practical use. This paper presents a neurodynamic approach to mitigate these challenges by transforming the robust linear programming to a non-smooth convex optimization through parameter elimination. A one-layer projection neural network with proven stability and convergence is proposed to solve the non-smooth optimization problem. The effectiveness of this approach is validated based on simulations of numerical examples and applications in reactor design and wastewater treatment.Robust optimization problems subject to norm uncertainty appear in numerous applications in various fields such as engineering, logistics, and finance. Despite its importance, robust optimization algorithms face significant computational challenges for solving high-dimensional problems, limiting their practical use. This paper presents a neurodynamic approach to mitigate these challenges by transforming the robust linear programming to a non-smooth convex optimization through parameter elimination. A one-layer projection neural network with proven stability and convergence is proposed to solve the non-smooth optimization problem. The effectiveness of this approach is validated based on simulations of numerical examples and applications in reactor design and wastewater treatment. |
| ArticleNumber | 108144 |
| Author | Wang, Jun Hu, Jin Zhou, Keying |
| Author_xml | – sequence: 1 givenname: Jin orcidid: 0000-0003-1535-0874 surname: Hu fullname: Hu, Jin email: jhu@cqjtu.edu.cn organization: School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China – sequence: 2 givenname: Keying orcidid: 0009-0008-3803-3146 surname: Zhou fullname: Zhou, Keying email: zky13536003576@163.com organization: School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China – sequence: 3 givenname: Jun orcidid: 0000-0002-1305-5735 surname: Wang fullname: Wang, Jun email: jwang.cs@cityu.edu.hk organization: Department of Computer Science and Departmebnt of Data Science, City University of Hong Kong, Hong Kong |
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| Keywords | Robust linear programming Nonsmooth optimization Norm uncertainty Neurodynamic optimization |
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