q-Line Search Scheme for Optimization Problem

In this paper new descent line search iterative schemes for unconstrained as well as constrained optimization problems are developed using q-derivative. At every iteration of the scheme, a positive definite matrix is provided which is neither exact Hessian of the objective function as in Newton scheme nor the positive definite matrix as generated in quasi-Newton scheme. Second order differentiablity property is not required in this process. Component of this matrix are constructed using q-derivative of the function. It is proved that the schemes preserve the property of Newton-like schemes in a local neighborhood of a minimum point which leads to the super linear rate of convergence. Numerical illustration of the scheme is also provided.