Neural-trust-region algorithm for unconstrained optimization (Part 1)
Optimization and Control
2020-05-26 v5
Abstract
In this paper (part 1), we describe a derivative-free trust-region method for solving unconstrained optimization problems. We will discuss a method when we relax the model order assumption and use artificial neural network techniques to build a computationally relatively inexpensive model. We directly find an estimate of the objective function minimizer without explicitly constructing a model function. Therefore, we need to have the neural-network model derivatives, which can be obtained simply through a back-propagation process.
Cite
@article{arxiv.2004.09058,
title = {Neural-trust-region algorithm for unconstrained optimization (Part 1)},
author = {Mostafa Rezapour and Thomas Asaki},
journal= {arXiv preprint arXiv:2004.09058},
year = {2020}
}