Non-differentiable optimization is a category of optimization that deals with objective that for a variety of reasons is non differentiable and thus non-convex. The functions in this class of optimization are generally non-smooth. These functions although continuous often contain sharp points or corners that do not allow for the solution of a tangent and are thus non-differentiable. In practice non-differentiable optimization encompasses a large variety of problems and a single one-size fits all solution is not applicable however solution is often reached through implementation of the subgradient method. Non-differentiable functions often arise in real world applications and commonly in the field of economics where cost functions often include sharp points. Early work in the optimization of non-differentiable functions was started by Soviet scientists Dubovitskii and Milyutin in the 1960'sand led to continued research by Soviet Scientists. The subject has been a continued field of study since with different theories and methods being applied to solution in different cases.

Last modified

• 11/30/2018

Creator

• Nathanael Robinson

DOI

• https://doi.org/10.21985/N2J75S

Keyword

• Differentiation Optimization

Rights statement

• In Copyright