On the Relationship between $\Lambda$-poisedness in Derivative-Free Optimization and Outliers in Local Outlier Factor

TL;DR

This paper explores how the concept of mbda-poisedness in derivative-free optimization relates to outliers detected by the Local Outlier Factor, highlighting implications for optimization robustness.

Contribution

It establishes a novel connection between mbda-poisedness in DFO and LOF outliers, providing insights into optimization stability and outlier detection.

Findings

mbda-poisedness influences LOF outlier detection in DFO

Outliers can indicate issues in the optimization process

The relationship aids in improving DFO robustness

Abstract

Derivative-free optimization (DFO) is a method that does not require the calculation of gradients or higher-order derivatives of the objective function, making it suitable for cases where the objective function is non-differentiable or the computation of derivatives is expensive. This communication discusses the importance of \(\Lambda\)-poisedness in DFO and the outliers detected by the Local Outlier Factor (LOF) on the optimization process. We discuss the relationship between \(\Lambda\)-poisedness in derivative-free optimization and outliers in local outlier factor.