L-BFGS is one of the widely used quasi-Newton methods. Instead of explicitly storing an approximation H of the inverse Hessian, L-BFGS keeps a limited number of vectors that can be used for computing the product of H by the gradient. However, this computation is sequential, each step depending on the outcome of the previous step. To solve this problem, we propose the Direct L-BFGS (DirL-BFGS) method that, seeing H as a linear operator, directly stores a low-rank plus diagonal (LRPD) representation of H. Employing the LRPD representation enables us to leverage the benefits of vector processing, leading to accelerating and parallelizing the calculations in the form of single instruction, multiple data.We evaluate our proposed method on different quadratic optimization problems and several regression and classification tasks with neural networks. Numerical results show that DirL-BFGS is faster overall than LBFGS.

Keywords