A note on the smoothing quadratic regularization method for non-Lipschitz optimization.

We present a new smaller upper bound for all the elements in the associate generalized Hessian used in the smoothing quadratic regularization (SQR) algorithm proposed by Bian and Chen (SIAM J. Optim. 23: 1718-1741, ). We modify the SQR algorithm by making use of the new upper bound. Numerical results show that our new upper bound improves the performance of the SQR algorithm significantly. [ABSTRACT FROM AUTHOR]