  A displacement aggregation strategy is applied in modified limited memory
Broyden Fletcher Goldfarb Shanno's (M-LBFGS) algorithm to solve large-scale
unconstrained optimization problems. A displacement aggregation helps to store
less memory while updating a new iterate to approximate the inverse Hessian
matrix as it discards the generating linear dependence vectors. It has been
observed that the M-LBFGS scheme achieves the same theoretical convergence
properties as the full memory scheme or the limited memory BFGS. Also,
Numerical results show that displacement aggregation in adaptive M-LBFGS
outperforms M-LBFGS.
