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A hybrid control framework for fast methods under invexity : Non-Zeno trajectories with exponential rate. / Sharifi Kolarijani, A.; Mohajerin Esfahani, P.; Keviczky, T.

Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2018). ed. / Andrew R. Teel; Magnus Egerstedt. Piscataway, NJ, USA : IEEE, 2018. p. 4078-4083.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Sharifi Kolarijani, A, Mohajerin Esfahani, P & Keviczky, T 2018, A hybrid control framework for fast methods under invexity: Non-Zeno trajectories with exponential rate. in AR Teel & M Egerstedt (eds), Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2018). IEEE, Piscataway, NJ, USA, pp. 4078-4083, CDC 2018: 57th IEEE Conference on Decision and Control, Miami, United States, 17/12/18. https://doi.org/10.1109/CDC.2018.8618707

APA

Sharifi Kolarijani, A., Mohajerin Esfahani, P., & Keviczky, T. (2018). A hybrid control framework for fast methods under invexity: Non-Zeno trajectories with exponential rate. In A. R. Teel, & M. Egerstedt (Eds.), Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2018) (pp. 4078-4083). Piscataway, NJ, USA: IEEE. https://doi.org/10.1109/CDC.2018.8618707

Vancouver

Sharifi Kolarijani A, Mohajerin Esfahani P, Keviczky T. A hybrid control framework for fast methods under invexity: Non-Zeno trajectories with exponential rate. In Teel AR, Egerstedt M, editors, Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2018). Piscataway, NJ, USA: IEEE. 2018. p. 4078-4083 https://doi.org/10.1109/CDC.2018.8618707

Author

Sharifi Kolarijani, A. ; Mohajerin Esfahani, P. ; Keviczky, T. / A hybrid control framework for fast methods under invexity : Non-Zeno trajectories with exponential rate. Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2018). editor / Andrew R. Teel ; Magnus Egerstedt. Piscataway, NJ, USA : IEEE, 2018. pp. 4078-4083

BibTeX

@inproceedings{6ec196501bf448adb707816aa4b38550,
title = "A hybrid control framework for fast methods under invexity: Non-Zeno trajectories with exponential rate",
abstract = "In this paper, we propose a framework to design a class of fast gradient-based methods in continuous-time that, in comparison with the existing literature including Nesterov's fast-gradient method, features a state-dependent, time-invariant damping term that acts as a feedback control input. The proposed design scheme allows for a user-defined, exponential rate of convergence for a class of nonconvex, unconstrained optimization problems in which the objective function satisfies the so-called Polyak-Łojasiewicz inequality. Formulating the optimization algorithm as a hybrid control system, a state-feedback input is synthesized such that a desired rate of convergence is guaranteed. Furthermore, we establish that the solution trajectories of the hybrid control system are Zeno-free.",
author = "{Sharifi Kolarijani}, A. and {Mohajerin Esfahani}, P. and T. Keviczky",
year = "2018",
doi = "10.1109/CDC.2018.8618707",
language = "English",
pages = "4078--4083",
editor = "Teel, {Andrew R. } and Egerstedt, {Magnus }",
booktitle = "Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2018)",
publisher = "IEEE",
address = "United States",

}

RIS

TY - GEN

T1 - A hybrid control framework for fast methods under invexity

T2 - Non-Zeno trajectories with exponential rate

AU - Sharifi Kolarijani, A.

AU - Mohajerin Esfahani, P.

AU - Keviczky, T.

PY - 2018

Y1 - 2018

N2 - In this paper, we propose a framework to design a class of fast gradient-based methods in continuous-time that, in comparison with the existing literature including Nesterov's fast-gradient method, features a state-dependent, time-invariant damping term that acts as a feedback control input. The proposed design scheme allows for a user-defined, exponential rate of convergence for a class of nonconvex, unconstrained optimization problems in which the objective function satisfies the so-called Polyak-Łojasiewicz inequality. Formulating the optimization algorithm as a hybrid control system, a state-feedback input is synthesized such that a desired rate of convergence is guaranteed. Furthermore, we establish that the solution trajectories of the hybrid control system are Zeno-free.

AB - In this paper, we propose a framework to design a class of fast gradient-based methods in continuous-time that, in comparison with the existing literature including Nesterov's fast-gradient method, features a state-dependent, time-invariant damping term that acts as a feedback control input. The proposed design scheme allows for a user-defined, exponential rate of convergence for a class of nonconvex, unconstrained optimization problems in which the objective function satisfies the so-called Polyak-Łojasiewicz inequality. Formulating the optimization algorithm as a hybrid control system, a state-feedback input is synthesized such that a desired rate of convergence is guaranteed. Furthermore, we establish that the solution trajectories of the hybrid control system are Zeno-free.

UR - http://www.scopus.com/inward/record.url?scp=85062171939&partnerID=8YFLogxK

U2 - 10.1109/CDC.2018.8618707

DO - 10.1109/CDC.2018.8618707

M3 - Conference contribution

SP - 4078

EP - 4083

BT - Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2018)

A2 - Teel, Andrew R.

A2 - Egerstedt, Magnus

PB - IEEE

CY - Piscataway, NJ, USA

ER -

ID: 51914802