A New Symmetric Rank One Algorithm for Unconstrained Optimization

Abstract

In this paper, a new symmetric rank one for unconstrained optimization problems is presented. This new algorithm is used to solve symmetric and positive definite matrix. The new method is tested numerically by (7) nonlinear test functions and method is compared with the standard BFGS algorithm. The new matrix used is symmetric and positive definite and it generates descent directions and satisfied QN-like condition.

Keywords

• unconstrained optimization
• descent direction
• qn-like condition