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A portfolio-based analysis method for competition results

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Dang_2022_ModRef2022_PortfolioBasedAnalysis_VoR.pdf (789.5Kb)
Date
31/07/2022
Author
Dang, Nguyen
Funder
The Leverhulme Trust
Grant ID
ECF-2020-168
Keywords
Algorithm portfolio
Algorithm selection
Constraint programming
QA75 Electronic computers. Computer science
NS
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Abstract
Competitions such as the MiniZinc Challenges or the SAT competitions have been very useful sources for comparing performance of different solving approaches and for advancing the state-of-the-arts of the fields. Traditional competition setting often focuses on producing a ranking between solvers based on their average performance across a wide range of benchmark problems and instances. While this is a sensible way to assess the relative performance of solvers, such ranking does not necessarily reflect the full potential of a solver, especially when we want to utilise a portfolio of solvers instead of a single one for solving a new problem. In this paper, I will describe a portfolio-based analysis method which can give complementary insights into the performance of participating solvers in a competition. The method is demonstrated on the results of the MiniZinc Challenges and new insights gained from the portfolio viewpoint are presented.
Citation
Dang , N 2022 , A portfolio-based analysis method for competition results . in ModRef 2022 - The 21st workshop on Constraint Modelling and Reformulation . The 21st workshop on Constraint Modelling and Reformulation , Haifa , Israel , 31/07/22 . < https://modref.github.io/papers/ModRef2022_PortfolioBasedAnalysisMethodForCompetitionResults.pdf >
 
workshop
 
Publication
ModRef 2022 - The 21st workshop on Constraint Modelling and Reformulation
Type
Conference item
Rights
Copyright © 2022 by the authors. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the final published version of the work, which was originally published at https://modref.github.io/papers/ModRef2022_PortfolioBasedAnalysisMethodForCompetitionResults.pdf.
Description
Nguyen Dang: is a Leverhulme Early Career Fellow.
Collections
  • University of St Andrews Research
URL
https://www-users.cs.york.ac.uk/~frisch/ModRef/
https://modref.github.io/papers/ModRef2022_PortfolioBasedAnalysisMethodForCompetitionResults.pdf
https://arxiv.org/abs/2205.15414
URI
http://hdl.handle.net/10023/25940

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