A combined conjugate-gradient quasi-Newton minimization algorithm

Although quasi-Newton algorithms generally converge in fewer iterations than conjugate gradient algorithms, they have the disadvantage of requiring substantially more storage. An algorithm will be described which uses an intermediate (and variable) amount of storage and which demonstrates convergence which is also intermediate, that is, generally better than that observed for conjugate gradient algorithms but not so good as in a quasi-Newton approach. The new algorithm uses a strategy of generating a form of conjugate gradient search direction for most iterations, but it periodically uses a quasi-Newton step to improve the convergence. Some theoretical background for a new algorithm has been presented in an earlier paper; here we examine properties of the new algorithm and its implementation. We also present the results of some computational experience.This research was supported by the National Research Council of Canada grant number A-8962.