  In recent years, the Deep Learning Alternating Minimization (DLAM), which is
actually the alternating minimization applied to the penalty form of the deep
neutral networks training, has been developed as an alternative algorithm to
overcome several drawbacks of Stochastic Gradient Descent (SGD) algorithms.
This work develops an improved DLAM by the well-known inertial technique,
namely iPDLAM, which predicts a point by linearization of current and last
iterates. To obtain further training speed, we apply a warm-up technique to the
penalty parameter, that is, starting with a small initial one and increasing it
in the iterations. Numerical results on real-world datasets are reported to
demonstrate the efficiency of our proposed algorithm.
