Global non-convex optimisation by polynomial model based optimisation (PMBO) for tumor response models

Abstract

We address global non-convex optimisation tasks by a novel approach given by modelling the expected improvement acquisition function by a polynomial. In contrast to classic Bayesian optimisation the method allows to follow analytic gradient descents identifying potential candidates of “optimal” samples. The approach is applied to a Tumor Response Model developed at the Valencia Lab, Life Science Department, BSC, demanding such a black box optimisation. As empirical results suggest the PMBO algorithm can identify optimal balance if Injection freunde, dose, and duration with much less simulation effort. The approach is intended to be extended to more (higher dimensional) complex optimisation tasks in this regard.