Efficient Line Search Method Based on Regression and Uncertainty Quantification

arXiv:2405.10897v1 Announce Type: cross
Abstract: Unconstrained optimization problems are typically solved using iterative methods, which often depend on line search techniques to determine optimal step lengths in each iteration. This paper introduces a novel line search approach. Traditional line search methods, aimed at determining optimal step lengths, often discard valuable data from the search process and focus on refining step length intervals. This paper proposes a more efficient method using Bayesian optimization, which utilizes all available data points, i.e., function …

abstract arxiv cs.lg iteration iterative line math.oc novel optimization paper quantification regression search type uncertainty