A Distributed Method for Optimal Capacity Reservation

Nicholas Moehle; Xinyue Shen; Zhi Quan Luo; Stephen Boyd

doi:10.1007/s10957-019-01528-5

A Distributed Method for Optimal Capacity Reservation

Nicholas Moehle, Xinyue Shen, Zhi Quan Luo, Stephen Boyd

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3 Scopus citations

Abstract

We consider the problem of reserving link capacity in a network in such a way that any of a given set of flow scenarios can be supported. In the optimal capacity reservation problem, we choose the reserved link capacities to minimize the reservation cost. This problem reduces to a large linear program, with the number of variables and constraints on the order of the number of links times the number of scenarios. We develop a scalable, distributed algorithm for the problem that alternates between solving (in parallel) one-flow problem per scenario, and coordination steps, which connect the individual flows and the reservation capacities.

Original language	English (US)
Pages (from-to)	1130-1149
Number of pages	20
Journal	Journal of Optimization Theory and Applications
Volume	182
Issue number	3
DOIs	https://doi.org/10.1007/s10957-019-01528-5
State	Published - Sep 15 2019
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

ADMM
Convex optimization
Network flow
Robust optimization

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AU - Shen, Xinyue

AU - Luo, Zhi Quan

AU - Boyd, Stephen

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Y1 - 2019/9/15

N2 - We consider the problem of reserving link capacity in a network in such a way that any of a given set of flow scenarios can be supported. In the optimal capacity reservation problem, we choose the reserved link capacities to minimize the reservation cost. This problem reduces to a large linear program, with the number of variables and constraints on the order of the number of links times the number of scenarios. We develop a scalable, distributed algorithm for the problem that alternates between solving (in parallel) one-flow problem per scenario, and coordination steps, which connect the individual flows and the reservation capacities.

AB - We consider the problem of reserving link capacity in a network in such a way that any of a given set of flow scenarios can be supported. In the optimal capacity reservation problem, we choose the reserved link capacities to minimize the reservation cost. This problem reduces to a large linear program, with the number of variables and constraints on the order of the number of links times the number of scenarios. We develop a scalable, distributed algorithm for the problem that alternates between solving (in parallel) one-flow problem per scenario, and coordination steps, which connect the individual flows and the reservation capacities.

KW - ADMM

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KW - Robust optimization

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A Distributed Method for Optimal Capacity Reservation

Abstract

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