RECURSIVE FORM OF GENERAL LIMITED MEMORY VARIABLE METRIC METHODS
In this report we propose a new recursive matrix formulation of limited memory variable metric methods. This approach can be used for an arbitrary update from the Broyden class (and some other updates) and also for the approximation of both the Hessian matrix and its inverse. The new recursive formulation requires approximately 4mn multiplications and additions per iteration, so it is comparable with other efficient limited memory variable metric methods. Numerical experiments concerning Algorithm 1, proposed in this report, confirm its practical efficiency.
recursive matrix formulation algorithms unconstrained optimization large scale optimization limited memory methods variable metric updates