Scaling Damped Limited-Memory Updates for Unconstrained Optimization, Journal of Optimization Theory and Applications.

This paper investigates scaling a modified limited-memory algorithm to solve unconstrained optimization problems. The basic idea was to combine the damped techniques for the limited-memory update and the technique of equilibrating the inverse Hessian matrix. Enhanced curvature information about the objective function is stored in the form of a diagonal matrix and plays the dual roles of providing an initial matrix and equilibrating for damped limited-memory iterations. Numerical experiments indicated that the new algorithm is very effective.