The Convergent Properties of a New Parameter for Unconstrained Optimization

Because of its simplicity, low memory requirement, computational cost, and global convergence properties, the Conjugate Gradient (CG) method is most popular iterative mathematical technique for optimizing both linear nonlinear systems. Some classical CG methods, however, have drawbacks such as poor numerical performance in terms iterations function evaluations. To address these shortcomings, researchers proposed new parameter variants with efficient results good properties. We present a conjugate gradient formula based on memoryless self-scale DFP quasi-Newton (QN) this paper. The fulfills sufficient descent property convergent condition any line research. When exact search used, reduced to HS formula. Finally, we conclude that our effective.