Robust Optimization for Selection of Genotypes with Maximum Genetic Gain
碩士 === 國立成功大學 === 數學系應用數學碩博士班 === 107 === In this thesis, we first review the three conic relaxation: SDP (Semi-definite programming), LP (Linear programming), and SOCP (Second-order cone programming), proposed in S. Safarina et al.(2017) for the optimum selection of genotypes that maximize genetic...
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| Format: | Others |
| Language: | en_US |
| Published: |
2019
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| Online Access: | http://ndltd.ncl.edu.tw/handle/g6v7bz |
| Summary: | 碩士 === 國立成功大學 === 數學系應用數學碩博士班 === 107 === In this thesis, we first review the three conic relaxation: SDP (Semi-definite programming), LP (Linear programming), and SOCP (Second-order cone programming), proposed in S. Safarina et al.(2017) for the optimum selection of genotypes that maximize genetic gain.
Then, we consider the robust optimization to the LP relaxation and incorporate with a steepest ascent method to acquire an appropriate solution for the equal deployment(ED) problem subject to uncertainty data.
At last, we conduct numerical experiments to test the feasibility incurred by the perturbation.
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