A fixed time distributed optimization: A sliding mode perspective

Chaojie Li; Xinghuo Yu; Xiaojun Zhou; Wei Ren

doi:10.1109/IECON.2017.8217439

A fixed time distributed optimization: A sliding mode perspective

Chaojie Li, Xinghuo Yu, Xiaojun Zhou, Wei Ren

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

27 Citations (Scopus)

Abstract

In this paper, a framework of convex optimization algorithm with a fixed time convergence rate is investigated. Given a strongly convex optimization problem, two control algorithms are developed to solve the problem within a fixed time of which the upper bound is theoretically obtained. Moreover, the fixed time convergence rate based algorithms are extended into the distributed manner which is applied to two typical distributed optimization problems including the resource allocation problem and the coordination optimization problem. Laplacian graph matrix is employed to the weighted gradient based and the coordination based distributed optimization algorithms. By developing the characteristic of the objective function, the upper bound of the fixed time convergence is derived. Two numerical examples are given to verify the main results. © 2017 IEEE.

Original language	English
Title of host publication	Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE
Pages	8201-8207
Volume	2017-January
ISBN (Print)	9781538611272
DOIs	https://doi.org/10.1109/IECON.2017.8217439
Publication status	Published - 15 Dec 2017
Externally published	Yes
Event	43rd Annual Conference of the IEEE Industrial Electronics Society (IECON 2017) - China National Convention Center, Beijing, China Duration: 29 Oct 2017 → 1 Nov 2017 http://iecon2017.csp.escience.cn/dct/page/1

Publication series

Name	Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
Volume	2017-January

Conference

Conference	43rd Annual Conference of the IEEE Industrial Electronics Society (IECON 2017)
Place	China
City	Beijing
Period	29/10/17 → 1/11/17
Internet address	http://iecon2017.csp.escience.cn/dct/page/1

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to <a href="mailto:[email protected]">[email protected]</a>.

Funding

This work was partially supported by the National Natural Science Foundation of China (Grant No. 61503416, 61533020, 61590921, 61673399) and the 111 Project (B17048).

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10.1109/IECON.2017.8217439

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Li, C., Yu, X., Zhou, X., & Ren, W. (2017). A fixed time distributed optimization: A sliding mode perspective. In Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society (Vol. 2017-January, pp. 8201-8207). (Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society; Vol. 2017-January). IEEE. https://doi.org/10.1109/IECON.2017.8217439

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abstract = "In this paper, a framework of convex optimization algorithm with a fixed time convergence rate is investigated. Given a strongly convex optimization problem, two control algorithms are developed to solve the problem within a fixed time of which the upper bound is theoretically obtained. Moreover, the fixed time convergence rate based algorithms are extended into the distributed manner which is applied to two typical distributed optimization problems including the resource allocation problem and the coordination optimization problem. Laplacian graph matrix is employed to the weighted gradient based and the coordination based distributed optimization algorithms. By developing the characteristic of the objective function, the upper bound of the fixed time convergence is derived. Two numerical examples are given to verify the main results. {\textcopyright} 2017 IEEE.",

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Li, C, Yu, X, Zhou, X & Ren, W 2017, A fixed time distributed optimization: A sliding mode perspective. in Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society. vol. 2017-January, Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society, vol. 2017-January, IEEE, pp. 8201-8207, 43rd Annual Conference of the IEEE Industrial Electronics Society (IECON 2017), Beijing, China, 29/10/17. https://doi.org/10.1109/IECON.2017.8217439

A fixed time distributed optimization: A sliding mode perspective. / Li, Chaojie; Yu, Xinghuo; Zhou, Xiaojun et al.
Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society. Vol. 2017-January IEEE, 2017. p. 8201-8207 (Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society; Vol. 2017-January).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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AB - In this paper, a framework of convex optimization algorithm with a fixed time convergence rate is investigated. Given a strongly convex optimization problem, two control algorithms are developed to solve the problem within a fixed time of which the upper bound is theoretically obtained. Moreover, the fixed time convergence rate based algorithms are extended into the distributed manner which is applied to two typical distributed optimization problems including the resource allocation problem and the coordination optimization problem. Laplacian graph matrix is employed to the weighted gradient based and the coordination based distributed optimization algorithms. By developing the characteristic of the objective function, the upper bound of the fixed time convergence is derived. Two numerical examples are given to verify the main results. © 2017 IEEE.

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A fixed time distributed optimization: A sliding mode perspective

Abstract

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