Robust Optimization for Selection of Genotypes with Maximum Genetic Gain

Abstract

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

Date of Award	2019
Original language	English
Supervisor	Ruey-Lin Sheu (Supervisor)