Max L. N. Gonçalves

Subsampled cubic regularization method with distinct sample sizes for function, gradient, and Hessian

Abstract We develop and study a subsampled cubic regularization method for finite-sum composite optimization problems, in which the function, gradient, and Hessian are estimated using possibly different sample sizes. By allowing each quantity to have its own sampling strategy, th...