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A displacement aggregation strategy is applied in modified limited memoryBroyden Fletcher Goldfarb Shanno's (M-LBFGS) algorithm to solve large-scaleunconstrained optimization problems. A displacement aggregation helps to storeless memory while updating a new iterate to approximate the inverse Hessianmatrix as it discards the generating linear dependence vectors. It has beenobserved that the M-LBFGS scheme achieves the same theoretical convergenceproperties as the full memory scheme or the limited memory BFGS. Also,Numerical results show that displacement aggregation in adaptive M-LBFGSoutperforms M-LBFGS.

Modified Limited memory BFGS with displacement aggregation