Optimization of Buffer Networks via DC Programming
This brief is concerned with the <inline-formula> <tex-math notation="LaTeX">H^{2} </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">H^{\infty } </tex-math></inline-formula> norm-constrained optimization p...
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| Vydané v: | IEEE Transactions on Circuits and Systems II: Express Briefs Ročník 70; číslo 2; s. 606 - 610 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
IEEE
01.02.2023
Institute of Electrical and Electronics Engineers (IEEE) The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1549-7747, 1558-3791 |
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| Abstract | This brief is concerned with the <inline-formula> <tex-math notation="LaTeX">H^{2} </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">H^{\infty } </tex-math></inline-formula> norm-constrained optimization problems of dynamic buffer networks. The extended network model is introduced first, wherein the weights of all edges can be tuned independently. Because of the emerging nonconvexity of the extended model, previous results of positive linear systems failed to address this situation. By resorting to the log-log convexity of a class of nonlinear functions called posynomials, the optimization problems can be reduced to differential convex programming problems. The proposed framework is illustrated for large-scale networks. |
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| AbstractList | This brief is concerned with the <inline-formula> <tex-math notation="LaTeX">H^{2} </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">H^{\infty } </tex-math></inline-formula> norm-constrained optimization problems of dynamic buffer networks. The extended network model is introduced first, wherein the weights of all edges can be tuned independently. Because of the emerging nonconvexity of the extended model, previous results of positive linear systems failed to address this situation. By resorting to the log-log convexity of a class of nonlinear functions called posynomials, the optimization problems can be reduced to differential convex programming problems. The proposed framework is illustrated for large-scale networks. This brief is concerned with the [Formula Omitted] and [Formula Omitted] norm-constrained optimization problems of dynamic buffer networks. The extended network model is introduced first, wherein the weights of all edges can be tuned independently. Because of the emerging nonconvexity of the extended model, previous results of positive linear systems failed to address this situation. By resorting to the log–log convexity of a class of nonlinear functions called posynomials, the optimization problems can be reduced to differential convex programming problems. The proposed framework is illustrated for large-scale networks. |
| Author | Zhao, Chengyan Ogura, Masaki Sakurama, Kazunori |
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| SubjectTerms | buffer network Buffers car-sharing service Convexity DC programming H² norm H∞ norm Linear systems Mathematical models Networks nonconvex optimization Optimization Positive linear system posynomial Pricing Programming Vehicle dynamics |
| Title | Optimization of Buffer Networks via DC Programming |
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