The q-Gradient Vector for Unconstrained Continuous Optimization Problems

In the beginning of nineteenth century, Frank Hilton Jackson generalized the concepts of derivative in the q -calculus context and created the q -derivative, widely known as Jackson’s derivative. In the q -derivative, the independent variable is multiplied by a parameter q and in the limit, q → 1, the q -derivative is reduced to the classical derivative. In this work we make use of the first-order partial q -derivatives of a function of n variables to define here the q -gradient vector and take the negative direction as a new search direction for optimization methods. Therefore, we present a q -version of the classical steepest descent method called the q -steepest descent method, that is reduced to the classical version whenever the parameter q is equal to 1. We applied the classical steepest descent method and the q -steepest descent method to an unimodal and a multimodal test function. The results show the great performance of the q -steepest descent method, and for the multimodal function it was able to escape from many local minima and reach the global minimum.