Conjugate Gradient Methods with Sufficient Descent Condition for Large-scale Unconstrained Optimization

摘要

In this paper, we make a modification to the standard conjugate gradient method so that its search direction satisfies the sufficient descent condition. We prove that the modified conjugate gradient method is globally convergent under Armijo line search. Numerical results show that the proposed conjugate gradient method is efficient compared to some of its standard counterparts for large-scale unconstrained optimization.