On the performance of switching BFGS/SR1 algorithms for unconstrained optimization

This paper studies some possible combinations of the best features of the quasi-Newton symmetric rank-one (SR1), BFGS and extra updating BFGS algorithms for solving nonlinear unconstrained optimization problems. These combinations depend on switching between the BFGS and SR1 updates so that certain desirable properties are imposed. The presented numerical results show that the proposed switching algorithm outperforms the robust BFGS method.