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Robust Optimal Contribution Selection

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出版年:arXiv.org (Dec 3, 2024), p. n/a
第一著者: Fogg, Josh
その他の著者: Ortiz, Jaime, Pocrnić, Ivan, Hall, J A Julian, Gorjanc, Gregor
出版事項:
Cornell University Library, arXiv.org
主題:
Solvers
Python
Robustness
Quadratic programming
Optimization
オンライン･アクセス:Citation/Abstract
Full text outside of ProQuest
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100 1 |a Fogg, Josh 
245 1 |a Robust Optimal Contribution Selection 
260 |b Cornell University Library, arXiv.org  |c Dec 3, 2024 
513 |a Working Paper 
520 3 |a Optimal contribution selection (OCS) is a selective breeding method that manages the conversion of genetic variation into genetic gain to facilitate short-term competitiveness and long-term sustainability in breeding programmes. Traditional approaches to OCS do not account for uncertainty in input data, which is always present and challenges optimization and practical decision making. Here we use concepts from robust optimization to derive a robust OCS problem and develop two ways to solve the problem using either conic optimization or sequential quadratic programming. We have developed the open-source Python package 'robustocs' that leverages the Gurobi and HiGHS solvers to carry out these methods. Our testing shows favourable performance when solving the robust OCS problem using sequential quadratic programming and the HiGHS solver. 
653 |a Solvers 
653 |a Python 
653 |a Robustness 
653 |a Quadratic programming 
653 |a Optimization 
700 1 |a Ortiz, Jaime 
700 1 |a Pocrnić, Ivan 
700 1 |a Hall, J A Julian 
700 1 |a Gorjanc, Gregor 
773 0 |t arXiv.org  |g (Dec 3, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3141258040/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2412.02888