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

arXiv: https://arxiv.org/abs/2407.17529 · HTML: https://mlsanity.com/paper/2407.17529/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2024.

## Built on by (most cited first, 1 of 1)

- [Why and When Neural Networks Improve Local Approximation in Optimization](https://mlsanity.com/paper/2608.24963/) (2026)

## Abstract (arXiv)

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.