Inexact basic tensor methods for some classes of convex optimization problems

Universal Regularization Methods: Varying the Power, the Smoothness and the Accuracy

(2019) Coralia Cartis et al.   SIAM JOURNAL ON OPTIMIZATION

Implementable tensor methods in unconstrained convex optimization

(2019) Yurii Nesterov   MATHEMATICAL PROGRAMMING

Relatively Smooth Convex Optimization by First-Order Methods, and Applications

(2018) Haihao Lu et al.   SIAM JOURNAL ON OPTIMIZATION

A Descent Lemma Beyond Lipschitz Gradient Continuity: First-Order Methods Revisited and Applications

(2017) Heinz H. Bauschke et al.   MATHEMATICS OF OPERATIONS RESEARCH

On High-order Model Regularization for Constrained Optimization

(2017) José Mario Martínez   SIAM JOURNAL ON OPTIMIZATION

Worst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models

(2016) E. G. Birgin et al.   MATHEMATICAL PROGRAMMING

Gradient methods for minimizing composite functions

(2012) Yu. Nesterov   MATHEMATICAL PROGRAMMING