Derivative-free optimization for custom loss functions

Derivative-free optimization (DFO) has emerged as a powerful technique for solving optimization problems where the gradient of the objective function is either unavailable, expensive to compute, or non-smooth. This article explores the application of DFO methods to optimize custom loss functions in machine learning and other fields. The paper also highlights the challenges and potential improvements in the current DFO approaches, offering insights for further research and practical applications.