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Algorithm engineering in robust optimization

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Algorithm engineering in robust optimization. / Goerigk, Marc; Schöbel, Anita.
Algorithm Engineering: Selected Results and Surveys. ed. / Lasse Kliemann; Peter Sanders. Springer, 2016. p. 245-279 (Lecture Notes in Computer Science; Vol. 9220).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Harvard

Goerigk, M & Schöbel, A 2016, Algorithm engineering in robust optimization. in L Kliemann & P Sanders (eds), Algorithm Engineering: Selected Results and Surveys. Lecture Notes in Computer Science, vol. 9220, Springer, pp. 245-279. https://doi.org/10.1007/978-3-319-49487-6

APA

Goerigk, M., & Schöbel, A. (2016). Algorithm engineering in robust optimization. In L. Kliemann, & P. Sanders (Eds.), Algorithm Engineering: Selected Results and Surveys (pp. 245-279). (Lecture Notes in Computer Science; Vol. 9220). Springer. https://doi.org/10.1007/978-3-319-49487-6

Vancouver

Goerigk M, Schöbel A. Algorithm engineering in robust optimization. In Kliemann L, Sanders P, editors, Algorithm Engineering: Selected Results and Surveys. Springer. 2016. p. 245-279. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-49487-6

Author

Goerigk, Marc ; Schöbel, Anita. / Algorithm engineering in robust optimization. Algorithm Engineering: Selected Results and Surveys. editor / Lasse Kliemann ; Peter Sanders. Springer, 2016. pp. 245-279 (Lecture Notes in Computer Science).

Bibtex

@inbook{83233811450447538ea600dcf7a3f02a,
title = "Algorithm engineering in robust optimization",
abstract = "Robust optimization is a young and emerging field of research having received a considerable increase of interest over the last decade. In this paper, we argue that the algorithm engineering methodology fits very well to the field of robust optimization and yields a rewarding new perspective on both the current state of research and open research directions.To this end we go through the algorithm engineering cycle of design and analysis of concepts, development and implementation of algorithms, and theoretical and experimental evaluation. We show that many ideas of algorithm engineering have already been applied in publications on robust optimization. Most work on robust optimization is devoted to analysis of the concepts and the development of algorithms, some papers deal with the evaluation of a particular concept in case studies, and work on comparison of concepts just starts. What is still a drawback in many papers on robustness is the missing link to include the results of the experiments again in the design.",
author = "Marc Goerigk and Anita Sch{\"o}bel",
year = "2016",
month = nov,
day = "11",
doi = "10.1007/978-3-319-49487-6",
language = "English",
isbn = "9783319494869",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "245--279",
editor = "Lasse Kliemann and Peter Sanders",
booktitle = "Algorithm Engineering",

}

RIS

TY - CHAP

T1 - Algorithm engineering in robust optimization

AU - Goerigk, Marc

AU - Schöbel, Anita

PY - 2016/11/11

Y1 - 2016/11/11

N2 - Robust optimization is a young and emerging field of research having received a considerable increase of interest over the last decade. In this paper, we argue that the algorithm engineering methodology fits very well to the field of robust optimization and yields a rewarding new perspective on both the current state of research and open research directions.To this end we go through the algorithm engineering cycle of design and analysis of concepts, development and implementation of algorithms, and theoretical and experimental evaluation. We show that many ideas of algorithm engineering have already been applied in publications on robust optimization. Most work on robust optimization is devoted to analysis of the concepts and the development of algorithms, some papers deal with the evaluation of a particular concept in case studies, and work on comparison of concepts just starts. What is still a drawback in many papers on robustness is the missing link to include the results of the experiments again in the design.

AB - Robust optimization is a young and emerging field of research having received a considerable increase of interest over the last decade. In this paper, we argue that the algorithm engineering methodology fits very well to the field of robust optimization and yields a rewarding new perspective on both the current state of research and open research directions.To this end we go through the algorithm engineering cycle of design and analysis of concepts, development and implementation of algorithms, and theoretical and experimental evaluation. We show that many ideas of algorithm engineering have already been applied in publications on robust optimization. Most work on robust optimization is devoted to analysis of the concepts and the development of algorithms, some papers deal with the evaluation of a particular concept in case studies, and work on comparison of concepts just starts. What is still a drawback in many papers on robustness is the missing link to include the results of the experiments again in the design.

U2 - 10.1007/978-3-319-49487-6

DO - 10.1007/978-3-319-49487-6

M3 - Chapter

SN - 9783319494869

T3 - Lecture Notes in Computer Science

SP - 245

EP - 279

BT - Algorithm Engineering

A2 - Kliemann, Lasse

A2 - Sanders, Peter

PB - Springer

ER -