  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.
