A Modified Globally Convergent Self-Scaling BFGS Algorithm for Unconstrained Optimization

Abstract In this paper, a modified globally convergent self-scaling BFGS algorithm for solving convex unconstrained optimization problems was investigated in which it employs exact line search strategy and the inverse Hessian matrix approximations were positive definite. Experimental results indicate that the new proposed algorithm was more efficient than the standard BFGS- algorithm.