A note on the accelerated proximal gradient method for nonconvex optimization


Wang, Huijuan and Xu, Hong-Kun


Abstract

carpathian_2018_34_3_449_457_abstract

We improve a recent accelerated proximal gradient (APG) method in [Li, Q., Zhou, Y., Liang, Y. and Varshney, P. K.,
 Convergence analysis of proximal gradient with momentum for nonconvex optimization, in
Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, PMLR 70, 2017] for nonconvex optimization by allowing variable stepsizes. We prove the convergence of the APG method for a composite nonconvex optimization problem under the assumption that the composite objective function satisfies the Kurdyka-Lojasiewicz property.

Additional Information

Author(s)

Wang, H., Xu, H.-K.