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Algorithm engineering in robust optimization

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Algorithm engineering in robust optimization. / Goerigk, Marc; Schöbel, Anita.
In: arxiv.org, 19.05.2015.

Research output: Contribution to Journal/Magazine › Journal article

Harvard

Goerigk, M & Schöbel, A 2015, 'Algorithm engineering in robust optimization', arxiv.org. <http://arxiv.org/abs/1505.04901>

APA

Goerigk, M., & Schöbel, A. (2015). Algorithm engineering in robust optimization. arxiv.org. http://arxiv.org/abs/1505.04901

Vancouver

Goerigk M, Schöbel A. Algorithm engineering in robust optimization. arxiv.org. 2015 May 19.

Author

Goerigk, Marc ; Schöbel, Anita. / Algorithm engineering in robust optimization. In: arxiv.org. 2015.

Bibtex

@article{58edacc891fc41238cb33489bc5cfbf4,
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 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.",
keywords = "math.OC, cs.DS, G.1.6; G.4",
author = "Marc Goerigk and Anita Sch{\"o}bel",
year = "2015",
month = may,
day = "19",
language = "English",
journal = "arxiv.org",

}

RIS

TY - JOUR

T1 - Algorithm engineering in robust optimization

AU - Goerigk, Marc

AU - Schöbel, Anita

PY - 2015/5/19

Y1 - 2015/5/19

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 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 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.

KW - math.OC

KW - cs.DS

KW - G.1.6; G.4

M3 - Journal article

JO - arxiv.org

JF - arxiv.org

ER -